System and method for optimally customizable and adaptive personalized information display for information associated with managing a chaotic event

ABSTRACT

A method for displaying information related to a chaotic event. A mathematical optimization algorithm is used to select an optimal decision set for a user. The mathematical optimization algorithm takes as input a decision template, chaotic event information regarding a chaotic event, and a user profile. The optimal decision set is displayed for the user.

RELATED APPLICATIONS

This application is a continuation-in-part of System and Method forManaging a Chaotic Event, U.S. application Ser. No. 11/516,954, filedSep. 7, 2006; and is also a continuation-in-part of System and Methodfor Optimizing Project Subdivision Using Data and Requirements FocusesSubject to Multidimensional Constraints, U.S. application Ser. No.11/553,526, filed Oct. 27, 2006.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to an improved data processingsystem. More particularly, the present invention relates to a computerimplemented method, apparatus, and computer usable program code formanaging a chaotic event.

2. Description of the Related Art

Major chaotic events are, by definition, times of great difficulty.Chaotic events are events that cause an interruption in routinesnormally performed by people in everyday activities because of damageinflicted to individuals and infrastructure. For example, there is greatpotential for episodes of profound chaos during hurricanes, earthquakes,tidal waves, solar flares, flooding, terrorism, war, and pandemics toname a few. Even when the chaotic event is statistically predictable,the results are often still shocking. Chaotic events do not occurfrequently, but the results may be long lasting and unexpected.

Human beings, by nature, are generally very ill prepared at a mentallevel for planning for and dealing with these chaotic events. Leadersand other planners tend to only concentrate on a small number of obvioussituations. Additionally, various chaotic events are difficult to planfor because of how rarely they occur and because of the unknowable. Theunknowable effects may include the severity and geographic range of theaffected area and the reaction to the event. Plans often have politicalor economic groundings rather than being empirically driven.

Further complicating chaotic events are the disruption to the lives ofstaff members, leaders of organizations, and individuals that may beexpected to provide support, services, or leadership during and afterthe chaotic event. Unfortunately, during chaotic events, the people mostneeded may have been killed, injured, assisting family members, fleeing,or otherwise inaccessible. Standard contingency planning, especially forexpert support, is necessary but insufficient because chaotic events arerare, catastrophic, and dynamic in nature.

The exact skills and quantities of each skill needed are unknowable. Theavailability of the necessary skill pool is problematic because tryingto lock in additional skills in advance of a chaotic event isfinancially and organizationally infeasible. Providing the logisticsnecessary in advance to provide expert support for all potentiallycatastrophes is impossible. As a result, people, corporations,governments, enterprises, and agencies have great difficulty in findingnecessary expert skills during chaotic events.

Additionally, the presentation of data to decision makers during achaotic effect can have a major impact on the effectiveness of thedecision makers. For example, during chaotic events decision makers canhave great difficulty making optimal decisions, from a mathematicallyverifiable perspective, even when theoretically optimal information isavailable. The decision maker can be overwhelmed or confused by the wayinformation is presented. The decision maker may also be unable toeasily find important and relevant pieces of information in a sea ofdata. These problems may be further compounded when decisions are madeby multiple decision makers.

SUMMARY OF THE INVENTION

The aspects of the present invention provide for a computer implementedmethod, apparatus, and computer usable program code for displayinginformation related to a chaotic event. A mathematical optimizationalgorithm is used to select an optimal decision set for a user. Themathematical optimization algorithm takes as input a decision template,chaotic event information regarding a chaotic event, and a user profile.The optimal decision set is displayed for the user.

Also provided is a method for optimally selecting a subset of decisionsfrom a first plurality of decisions related to management of a chaoticevent. The first plurality of decisions related to the chaotic event isreceived. A heuristic algorithm is used to eliminate a first subset ofdecisions. The first subset of decisions is in the first plurality ofdecisions. A second plurality of decisions is formed. The secondplurality of decisions comprises the first plurality of decisions lessthe first subset of decisions. A mathematical optimization algorithm isthen used to select a second subset of decisions. The second subset ofdecisions is within the second plurality of decisions. The mathematicaloptimization algorithm takes as input at least one constraint andchaotic event information. The second subset of decisions is stored.

Also provided is a method for determining a sequence of decisionsrelated to a chaotic event. A plurality of decisions related to thechaotic event is received. A mathematical optimization algorithm is usedto select a sequence in which the plurality of decisions are to beconsidered. The mathematical optimization algorithm takes as input atleast one constraint and chaotic event information. The sequence isstored.

Also provided is a method for displaying information related to achaotic event. A mathematical optimization algorithm is used to select afirst optimal decision set for a user. The mathematical optimizationalgorithm takes as input a decision template, chaotic event information,at least one constraint, and a user profile. A heuristic algorithm isused to eliminate a first subset of decisions. The first subset ofdecisions is in the first optimal decision set. A second optimaldecision set is formed. The second optimal decision set comprises thefirst optimal decision set less the first subset of decisions. Themathematical optimization algorithm is used to select a sequence inwhich decisions in the second optimal decision set are to be considered.The mathematical optimization algorithm takes as input the secondoptimal decision set, the decision template, the chaotic eventinformation, the at least one constraint, and the user profile. Thesequence is stored.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 is a block diagram for managing chaotic events in accordance withthe illustrative embodiments;

FIG. 4 is a block diagram for detecting chaotic events in accordancewith the illustrative embodiments;

FIG. 5 is a block diagram for predicting severity of chaotic events inaccordance with the illustrative embodiments;

FIG. 6 is a block diagram for finding and organizing skills for chaoticevents in accordance with the illustrative embodiments;

FIG. 7 is a block diagram for finding and organizing routes for chaoticevents in accordance with the illustrative embodiments;

FIG. 8 is a flowchart for managing expert resources during times ofchaos in accordance with the illustrative embodiments;

FIG. 9 is a block diagram illustrating a major information technologyproject, in accordance with an illustrative embodiment;

FIG. 10 is a block diagram of a prior art method of constructing a majorinformation technology project;

FIG. 11 is a block diagram of a set of sub-projects created using theprior art method shown in FIG. 10;

FIG. 12 is a block diagram illustrating major information technologysub-projects that inefficiently overlap underlying realities of existinginformation technology systems as a result of the prior art method shownin FIG. 10;

FIG. 13 is a block diagram illustrating a computer-implemented method ofcreating optimized sub-projects for a major information technologyproject, in accordance with an illustrative embodiment;

FIG. 14 is a block diagram illustrating optimally selected sub-projectsfor a major information technology project, in accordance with anillustrative embodiment;

FIG. 15 is a block diagram illustrating major information technologysub-projects that efficiently overlap underlying realities of existinginformation technology systems, in accordance with an illustrativeembodiment;

FIG. 16 is a block diagram illustrating a method of creating optimizedsub-projects for a major information technology project, in accordancewith an illustrative embodiment;

FIG. 17 is an exemplary output object valuation matrix, in accordancewith an illustrative embodiment;

FIG. 18 is a block diagram of a “to be” data model, in accordance withan illustrative embodiment;

FIG. 19 is a block diagram of a “to be” process model, in accordancewith an illustrative embodiment;

FIG. 20 is a block diagram illustrating data value clusters, inaccordance with an illustrative embodiment;

FIG. 21 is a block diagram illustrating process value clusters, inaccordance with an illustrative embodiment;

FIG. 22 is a block diagram illustrating elements of a “to be” datamodel, in accordance with an illustrative embodiment;

FIG. 23 is a block diagram illustrating elements of a “to be” processmodel, in accordance with an illustrative embodiment;

FIG. 24 is an exemplary affinity matrix, in accordance with anillustrative embodiment;

FIG. 25 is a block diagram illustrating mapping from an “as-is” model toa “to be” model, in accordance with an illustrative embodiment;

FIG. 26 is a block diagram illustrating transformation issues applied tothe mapping from an “as-is” model to a “to be” model, in accordance withan illustrative embodiment;

FIG. 27 is a block diagram illustrating exemplary available resources,in accordance with an illustrative embodiment;

FIG. 28 is a block diagram illustrating exemplary project constraints,in accordance with an illustrative embodiment;

FIG. 29 is a block diagram illustrating exemplary political concerns, inaccordance with an illustrative embodiment;

FIG. 30 is a block diagram illustrating examples of feedback applied toan optimization engine, in accordance with an illustrative embodiment;

FIG. 31 is a block diagram illustrating a computer-implemented method ofcreating optimized sub-projects for a major information technologyproject, in accordance with an illustrative embodiment;

FIG. 32 is a flowchart illustrating a computer-implemented method ofcreating optimized sub-projects for a major information technologyproject, in accordance with an illustrative embodiment;

FIG. 33 is a block diagram of a system for chaotic event management, inaccordance with an illustrative embodiment;

FIG. 34 is a block diagram of an additional function for a system forchaotic event management, in accordance with an illustrative embodiment;

FIG. 35 is an exemplary screenshot of an output of a system for chaoticevent management, in accordance with an illustrative embodiment;

FIG. 36 is an exemplary screenshot of an output of a system for chaoticevent management, in accordance with an illustrative embodiment;

FIG. 37 is an exemplary screenshot of an output for a system for chaoticevent management, in accordance with an illustrative embodiment;

FIG. 38 is a flowchart illustrating an operation of a system for chaoticevent management, in accordance with an illustrative embodiment;

FIG. 39 is a flowchart illustrating a process of sub-dividing a decisionset, in accordance with an illustrative embodiment;

FIG. 40 is a flowchart of a process of sequencing a set of decisions, inaccordance with an illustrative embodiment;

FIG. 41 is a flowchart illustrating a process of generating optimaldecision sets, in accordance with an illustrative embodiment;

FIG. 42 is a flowchart illustrating a process of generating a set ofdecisions, in accordance with an illustrative embodiment;

FIG. 43 is a flowchart illustrating a process of optimizing a sequenceof decisions, in accordance with an illustrative embodiment;

FIG. 44 is a flowchart illustrating a process of generating an optimalsequence of decisions, in accordance with an illustrative embodiment;

FIG. 45 is a flowchart illustrating a process of generating andsequencing an optimal decision set, in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

I. Preface

This document is divided into six major sections. This section, SectionI is the preface and describes the overall organization of thisdocument. The second section, Section II, provides general backgroundknowledge of networks and computers. The third section, Section III,describes our prior work with regard to the management of chaoticevents. The fourth section, Section IV, describes our prior work withregard to mathematically rigorous optimal selection of subprojects for amajor project. The fifth section, Section V, describes our additionalwork in the area of management of chaotic events. The sixth section,Section VI, is the conclusion section. The terms “our” and “we” refer tothe inventors of the material in this document and/or of the materialsin the related documents described below. The terms “we” and “our” canrefer to a singular inventive entity, a single person, or multiplepersons where appropriate.

II. Computer and Network Background

This section, Section II, provides general background knowledge ofcomputers and networks. The following section, Section III, describesour prior work in the area of chaotic event management.

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented. Networkdata processing system 100 is a network of computers in whichembodiments may be implemented. Network data processing system 100contains network 102, which is the medium used to provide communicationslinks between various devices and computers connected together withinnetwork data processing system 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. These clients 110, 112, and 114 may be, forexample, personal computers or network computers. In the depictedexample, server 104 provides data, such as boot files, operating systemimages, and applications to clients 110, 112, and 114. Clients 110, 112,and 114 are clients to server 104 in this example. Network dataprocessing system 100 may include additional servers, clients, and otherdevices not shown.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation fordifferent embodiments.

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented. Data processing system 200is an example of a computer, such as server 104 or client 110 in FIG. 1,in which computer usable code or instructions implementing the processesmay be located for the illustrative embodiments.

In the depicted example, data processing system 200 employs a hubarchitecture including a north bridge and memory controller hub (MCH)202 and a south bridge and input/output (I/O) controller hub (ICH) 204.Processor 206, main memory 208, and graphics processor 210 are coupledto north bridge and memory controller hub 202. Graphics processor 210may be coupled to the MCH through an accelerated graphics port (AGP),for example.

In the depicted example, local area network (LAN) adapter 212 is coupledto south bridge and I/O controller hub 204 and audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) ports and other communications ports 232, andPCI/PCIe devices 234 are coupled to south bridge and I/O controller hub204 through bus 238, and hard disk drive (HDD) 226 and CD-ROM drive 230are coupled to south bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM drive230 may use, for example, an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. A super I/O(SIO) device 236 may be coupled to south bridge and I/O controller hub204.

An operating system runs on processor 206 and coordinates and providescontrol of various components within data processing system 200 in FIG.2. The operating system may be a commercially available operating systemsuch as Microsoft® Windows® XP (Microsoft and Windows are trademarks ofMicrosoft Corporation in the United States, other countries, or both).An object oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java programs or applicationsexecuting on data processing system 200 (Java and all Java-basedtrademarks are trademarks of Sun Microsystems, Inc., in the UnitedStates, other countries, or both).

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as hard disk drive 226, main memory 208, tape drives, or any otherform of memory or storage for data, and may be loaded into main memory208 for execution by processor 206. The processes of the illustrativeembodiments may be performed by processor 206 using computer implementedinstructions, which may be located in a memory such as, for example,main memory 208, read only memory 224, or in one or more peripheraldevices.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. Also, the processes of the illustrative embodiments may be appliedto a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may be comprised of oneor more buses, such as a system bus, an I/O bus and a PCI bus. Of coursethe bus system may be implemented using any type of communicationsfabric or architecture that provides for a transfer of data betweendifferent components or devices attached to the fabric or architecture.A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache such as found in north bridgeand memory controller hub 202. A processing unit may include one or moreprocessors or CPUs. The depicted examples in FIGS. 1-2 andabove-described examples are not meant to imply architecturallimitations. For example, data processing system 200 also may be atablet computer, laptop computer, or telephone device in addition totaking the form of a PDA.

III. Our Prior Work in the Area of Chaotic Event Management

The previous section, Section II, describes computers and networksgenerally. This section, Section III, describes our prior work in thearea of chaotic event management. The following section, Section IV,describes our prior work in the area of optimized selection ofsub-projects for a major information technology project.

Illustrative embodiments provide a computer implemented method,apparatus, and computer usable program code for managing a chaoticevent. A chaotic event is detected automatically or manually based onreceived information. The process of the illustrative embodiments isinitiated in response to the detection of a potentially chaotic event.In general terms, management of the event begins from a single point ormultiple points, based on the detection of a potentially chaoticsituation. A determination is made as to what the required resources arefor the situation.

Resources or expert resources are skills, expert skills, and resourcesrequired by individuals with skills to deal with the chaotic event.Resources include each expert individual with the necessary skills aswell as transportation, communications, and materials to properlyperform the task required by the expertise or skill of the individual.For example, heavy equipment operators may be needed as well as doctors.Heavy equipment operators may need bulldozers, backhoes, andtransportation to the event location, and the doctors may requirenurses, drugs, a sterile room, a communications center, emergencyhelicopters, and operating instruments.

The needed skills are optimized based on requirements and constraintsfor expert services, a potential skills pool, cohorts of a related setof skills, and enabling resources. Optimization is the process offinding a solution that is the best fit based on the available resourcesand specified constraints. The solution is skills and resources that areavailable and is recognized as the best solution among numerousalternatives because of the constraints, requirements, and othercircumstances and criteria of the chaotic event. A cohort or unifiedgroup may be considered an entity rather than a group of individualskills, such as a fully functioning mobile army surgical hospital (MASH)unit.

The service requirements are transmitted to the management location forreconciliation of needed skills against available skills. Skillsrequirements and individuals and cohorts available for deployment areselected based on optimization of costs, time of arrival, utility value,capacity of transportation route, and value. Routes are how the resourceis delivered. For example, in some cases, a route is an airplane. Inanother example, a route is a high-speed data line that allows a surgeonto remotely view an image. The process is continuously monitored andoptimized based on feedback and changing situations. The execution ofthe plan is implemented iteratively to provide the necessary expertresources. The expert resources are deployed by decision makers tomanage the chaotic event by effectively handling the circumstances,dangers, events, and problems caused by the chaotic event.

FIG. 3 is a block diagram for managing chaotic events in accordance withthe illustrative embodiments. Event management system 300 is acollection or network of computer programs, software components ormodules, data processing systems, devices, and inputs used to manageexpert skills for a chaotic event. Event management system 300 includesall steps, decisions, and information that may be needed to deal with achaotic event. Event management system 300 may be a centralized computerprogram executed and accessible from a server, such as server 104 ofFIG. 1 or a network of hardware and software components, such as networkdata processing system 200 of FIG. 2.

Event management system 300 or portions of event management system 300may be stored in a databases or data structures, such as storage 108 ofFIG. 1. Event management system 300 may be accessed in person or byusing a network, such as network 102 of FIG. 1. Event management system300 may be accessed by one or more users, decision makers, or eventmanagers for managing the chaotic event. The user may enter informationand receive information through an interface of event management system300. The information may be displayed to the user in text and graphics.Additionally, the user may be prompted to enter information anddecisions to help the user walk through the management of the chaoticevent. For example, event management system 300 may walk a stategovernor through each step that should be taken for a sun flare that hascrippled the state in a logical and effective sequence.

Event management system 300 is used for information processing so thatdecisions may be more easily made based on incoming information that isboth automatically sent and manually input. Event management system 300enables administrators, leaders, and other decision makers to makedecisions in a structured and supported framework. In some cases,leaders may be so unprepared or shocked by the chaotic event that eventmanagement system 300 may walk leaders through necessary steps. In thismanner, event management system 300 helps the leaders to take effectiveaction quickly. Event management system 300 intelligently interacts withdecision makers providing a dynamic interface for prioritizing steps anda work flow for dealing with the chaotic event in a structuredframework. The decisions may be based on policy and politics in additionto logistical information.

Event management system 300 is managed by event management 302. Eventmanagement 302 begins the process of managing a chaotic event inresponse to event detection 304 detecting the event. For example, if thechaotic event is a series of catastrophic tornadoes, event detection 304may become aware of the tornadoes through the national weather service.Alternatively, storm chasers may witness the series of tornadoes andreport the event in the form of manual input 306 to event detection 304.Event detection 304 may also be informed of the chaotic event by sensordata 308. Sensor data is information from any number of sensors fordetecting chaotic events including sensors for detecting wind, rain,seismic activity, radiation, and so forth. Event detection 304 informsevent management 302 of the chaotic event occurrence and known detailsof severity so that preliminary estimates may be made. Event detection304 is further described in FIG. 4, and predicting severity of chaoticevents is further described in FIG. 5 below.

Once event detection 304 has informed event management 302 of thelocation and occurrence of a chaotic event, event management 302 workswith management location 310 to determine a suitable location formanagement of the event. Event detection 304 sends a message to eventmanagement 302. The message may specify any ascertained information,such as the time, focal point, geographic area, and severity of thechaotic event if known. For example, if event management 302 is locatedon server 104 of FIG. 1 that has been flooded by torrential rains inGeorgia, event management 302 may be transferred to server 106 of FIG.1, located in Texas. Management location 310 allows the process of eventmanagement 302 to occur from the best possible location. Eventmanagement 302 may occur from multiple event management positions ifthere are multiple chaotic events simultaneously.

For example, the best possible location may be an external location outof the danger zone or affected area. Alternatively, the best possiblelocation may be the location closest to the affected area that still hasaccess to power, water, communications, and other similar utilities.Management location 310 may maintain a heartbeat connection with a setof one or more event management positions for immediately transferringcontrol to a specified event management component if the heartbeatconnection is lost from an event management component in the affectedarea. The heartbeat signal should be an encrypted signal.

A heartbeat connect is a periodic message or signal informing otherlocations, components, modules, or people of the status of eventmanagement 302. In another example, the chaotic event may be a federaldisaster. A local management location 310 may transfer control of eventmanagement 302 to the headquarters of the supervising federal agency,such as Homeland Security or the Federal Aviation Administration (FAA).If event management 302 is damaged or inaccessible, a redundant oralternative event management location automatically takes control.Additionally, event management 302 may systematically make decisionsregarding event management or transfer management location 310 to adifferent location if event management 302 does not receive instructionsor feedback from decision makers or other individuals involved inmanagement of the chaotic event.

For example, if a mayor providing user input and information from eventmanagement 302 becomes unavailable, decisions regarding management maybe made based on the best available information and alternatives.Additionally, management location 310 may be transferred to a locationwhere individuals are able and willing to provide user input and receiveinformation from event management 302.

In some cases, such as a large chemical release, leaders forcorporations, organizations, and government entities may not have directaccess to event management 302. As a result, message routing group 312may be used to communicate instructions 314 for the effective managementof the chaotic event. Message routing group 312 is the hardware andsoftware system used to communicate instructions 314 from eventmanagement 302. Instructions 314 may include directions, instructions,and orders for managing the response and other event-specificinformation.

Message routing group 312 may keep track of whether instructions 314have been received by the intended party through the tracking ofdelivery status 316. Delivery status 316 indicates status information,such as if, when, how the message in instructions 314 was delivered, anddescriptions of any problems preventing delivery.

Event management 302 passes information about the event to eventrequirements 318. For example, event management 302 may pass informationregarding the severity of the chaotic event gleaned from manual input306 and sensor data 308 to event requirements 318. Event requirements318 determine which skills, resources, or other information is requiredfor the chaotic event. Event requirements 318 determine whether requiredskills and resources may be provided in person or remotely. For example,welders and trauma doctors may be required to be in person, but apathologist may work via remote microscope cameras and a high-speed dataconnection.

Event requirements 318 may be updated by event management 302 as moreinformation becomes available about the chaotic event. Eventrequirements 318 may use event type skills 320 to determine the skillsneeded based on the type of chaotic event. Event type skills 320 is acollection of resources needed for each event type. For example, if ahurricane has damaged water-retaining facilities, such as reservoirs,levees, and canals, more civil engineers than normal may be required forthe hurricane. Event type skills 320 is preferably a database of skillsstored in a database or memory, such as main memory 208 of FIG. 2required for all possible chaotic events. For example, event type skills320 may specify the skills needed for a meltdown of a nuclear reactorincluding welders, waste disposal experts, nuclear engineers,paramedics, doctors, nuclear researchers, and so forth.

Event requirements 318 may also receive information regarding requiredskills in the form of manual input 322. Manual input 322 may be receivedfrom authorized individuals close to the chaotic event, experts in thefield, or based on other in-field or remote observations.

Information from event requirements 318 is passed to availability 319.Availability 319 performs a preliminary determination of the skills andresources to determine available skills and resources. For example,experts with required skills may be called, emailed, or otherwisecontacted to determine whether the expert is available, and if so, forhow long and under what conditions or constraints. Individuals ororganizations with manage, access, control, or possess resources arecontacted to determine whether the resources may be used. Availability319 may also rank potential skills and resources based on location,availability, proximity, cost, experience, and other relevant factors.Availability information is passed from availability 319 to optimizationroutines 324.

Optimization routines 324 uses information from availability 319,requirements and constraints 326, potential skills 328, and enablingresources 330 to iteratively make suggestions regarding optimal skillsand resources. Iterations are based particularly on event severity andevent type. For example, optimization routines 324 may be used onceevery six minutes at the onset of a chaotic event whereas after threemonths, the iterations may be updated once a day. Only skills andresources that may be available are considered by optimization routines324. Optimal skills and resources are derived based on elapsed time toarrive on-scene, proximity, capacity, importance, cost, time, and value.For example, optimal location for skills may be preferentially orderedby skill type and value or estimated time of arrival to the scene of thechaotic event.

Optimization routines 324 is a process for maximizing an objectivefunction by systematically choosing the values of real or integervariables from within an allowed set. The values used by optimizationroutines are values assigned to each skill, resource, route, and otherfactors that relate to delivery of the required skills and resources.

In one example, optimization routines 324 may be described in thefollowing way:

Given: a function ƒ: A-R from some set A

Sought: an element x₀ such that ƒ(x₀)≧ƒ(x) for all x in A

Typically, A is some subset of the Euclidean space R^(n), oftenspecified by a set of constraints, equalities or inequalities that themembers of A have to satisfy. For example, constraints may includecapacity, time, and value. For example, the capacity of a truck and ahelicopter are different as are a dial-up Internet connection and acable Internet connection.

The elements of A are called feasible solutions. The function ƒ, that ismaximized, is called an objective function or cost function. A feasiblesolution that maximizes the objective function is called an optimalsolution and is the output of optimization routines 324 in the form ofoptimized skills and resources. Optimal skills and resources are theresources that are the best solution to a problem based on constraintsand requirements. For example, the problem or skill to be optimized maybe that event managers need a doctor with a specialty in radiationsickness with three or more years experience in or around Texas withtransportation to Dallas, Tex. that is available for the next two weeks.The optimal solution in this case may be a doctor that lives in NorthernDallas with the required experience and availability. The optimalsolution for skills and resources is also optimized based on cost. If abulldozer may be moved from two locations with similar restraints, theoptimal solution is the cheapest solution. In other words, all otherconstraints being met, a lower cost resource is preferably to a highercost resource. Aspects of optimization routines 324 are furtherdescribed in FIG. 6 for finding and organizing skills.

Requirements and constraints 326 specify the requirements andconstraints for expert services. Requirements and constraints 326 may beestablished by local and federal law, organizational ethics, or othersocietal norms and policies. Similarly, requirements and constraints 326may be adjusted by persons in authority based on the needs and urgencyof those needs. For example, during a biological disaster, there may bea requirement that only individuals immunized for small pox be allowedto provide services. Additionally, requirements and constraints 326 mayinitially suggest that only medical doctors with three or more years ofpractice will be beneficial for the chaotic event. Requirements andconstraints 326 may be adjusted as needed, removed, or replaced with anew looser restraint. Decision makers should be informed about thebinding constraints, such as license required.

Requirements and constraints 326 may be dynamically adjusted based onconditions of the disaster. For example, if there is an extreme outbreakof small pox, constraints and requirements 326 may specify that anydoctor immunized for smallpox, regardless of experience, would be usefulfor dealing with the small pox outbreak. Requirements and constraints326 may be specified by governmental, public health, or businessrequirements.

Potential skills 328 specify the potential expert skills of individualsthat may be available. Potential skills 328 may be generated based oncommercial or governmental databases, job sites, research and papers,public licenses, or using a web crawler. For example, OmniFind producedby International Business Machines Corporation.

Enabling resources 330 are the resources that enable qualified expertsto perform the required tasks. Enabling resources 330 may be manuallygenerated by experts in each field or may be automatically generatedbased on past events. Enabling resources 330 may be stored in a databaseor storage, such as 108 of FIG. 1. For example, if a bomb has partiallydestroyed a building, a structural engineer may require the use of aconcrete X-ray machine to properly perform the tasks that may berequired. In another example, a heart surgeon may instruct a generalsurgeon how to perform specialized procedures using high resolutionweb-cameras. As a result, enabling resources 330 needs to have access toa data connection, including landlines or wireless communications at aspecified bandwidth, and cameras, as well as a sterile location, medicalequipment, and personnel to perform the procedure. In yet anotherexample, doctors remotely servicing the outbreak of a virus may requireemail access to digital pictures taken by medical technicians in thearea of the chaotic event.

Optimization routines 324 computes the optimum mix of skills andresources. The answer will consist of the person and/or resources,transportation routes to the disaster site, time of availability, andthe shadow price of substituting an alternate resource. Optimizationroutines 324 specifies alternatives in case an optimum skill andresource is unavailable. As a result, the next most optimal skill andresource may be quickly contacted until the necessary skills andresources are found to manage the chaotic event.

Availability 319 and verify availability 332 determines which expertsand resources are available automatically or based on manual input 334.In these examples, manual input 334 may be received as each individualor group responsible for the expert or resource is contacted and termsof availability are checked. Manual inputs 306, 322, and 334 may besubmitted via phone, email, or other voice, text, or data recognitionsystem. Alternatively, availability 319 and verify availability 332 mayuse an automatic message system to contact each expert to determineavailability. For example, using pre-collected email addresses for theexperts, an automated messaging system may request availabilityinformation from experts with the desired skill set. For example, theCenters for Disease Control (CDC) may have a database of expertsspecifying personal information, for example, addresses, contactinformation, and inoculation history that may be used to contactrequired experts and professionals.

Verify availability 332 determines whether the optimized skills andresources are available. Verify availability 332 confirms that theskills and resources selected by event management 302 to manage thechaotic event will in fact be available and may be relied on. Forexample, a surgical team that is selected by optimization routines 324as the best fit for an earthquake trauma team may need to be called onthe phone to confirm that the surgical team may be flown to theearthquake site in exactly twenty four hours. Once verify availability332 has determined which experts and resources are available, thatinformation is passed to event management 302.

The process for updating event requirements 318, availability 319,optimization routines 324, and verify availability 332 are repeatediteratively based on information regarding the chaotic event. Forexample, after an earthquake affecting the San Francisco area, eventrequirements 318 may be updated every eight hours for two months untilall of the required needs and skills have been acquired.

FIG. 4 is a block diagram for detecting chaotic events in accordancewith the illustrative embodiments. Event detection system 400 may beimplemented in an event detection component, such as event detection 304of FIG. 3. Alternatively, event detection system 400 may be part of anevent management module, such as event management 302 of FIG. 3. Eventdetection system 400 is the system used to detect a potentially chaoticevent. Event detection system 400 may determine whether an event isreal, and if so, whether the event is significant. For example, anundersea earthquake may or may not be a chaotic event based on location,size of the earthquake, and the potential for a tsunami.

Event detection 402 functions using various techniques and processes todetect a potentially chaotic event. Event detection 402 may become awareof the chaotic event through external service 404. External service 404may be a government, business, or other organizational monitoringservice. For example, external service 404 may include the NationalTransportation Board, National Weather Service, National HurricaneService, news wire services, Lloyds of London for loss of ships, theBloomberg service, or Guy Carpenter insurance database, and othercommercial information brokers.

Event detection 402 may also receive manual input 406, such as manualinput 306 of FIG. 3 as previously described. Manual input 306 may alsobe used to verify whether a chaotic event has actually occurred. Crawlerand semantic search 406 may be used to access Internet 408. Crawler andsemantic search 406 is a web crawler that searches publicly availableportions of the Internet for keywords or other indications that achaotic event has, is, or will occur. A web crawler is a program whichbrowses Internet 408 in a methodical, automated manner. For example, theweb crawler may note email traffic, news stores, and other forms of datamining. False alarms are filtered out with heuristic rules andman-in-the-loop functions.

Similarly, voice to text semantic search 410 may be used to identifythat a chaotic event has taken place. Voice to text semantic search 410may use voice to text translations or voice recognition technologies torecognize phrases, keywords, or other indicators of a chaotic event. Forexample, transmissions across emergency broadcast channels or toemergency services may be analyzed by voice to text semantic search toidentify that a reservoir has broken.

Event detection 402 may also receive input from sensor data 412. Sensordata 412 is data, such as sensor data 308 of FIG. 3. Sensor data 412 maybe received from sensors 414 which may include physical sensors 416,such as sensors that monitor gaps in bridges, seismic sensors 418 formonitoring seismic activity, current sensors 420 such as current sensorsin utility lines for detecting electromagnetic pulses, water levelsensors 422, and solar monitoring sensors 424 for indicating solaractivity. Sensors 414 are used to automatically pass sensor data 412indicating a chaotic event to event detection 402. Sensors 414 may alsoinclude monitors to indicate total loss of communications via internetor telephone to a given area, absolute volumes coming out of aparticular area, spikes or communications jams, failures of cell phonetowers, and other occurrences that indicate a chaotic event may haveoccurred.

Event detection 402 outputs the event detection to timing and severityprediction 426. Timing and severity prediction 426 indicates the knowntiming and severity of the chaotic event or a predicted time andseverity if the chaotic event is anticipated. Timing and severityprediction 426 may receive information via manual input 428. Forexample, a scientist measuring seismic activity may send data and visualinformation regarding the eruption of a volcano to indicate the severityof the event. Timing and severity prediction 426 passes the informationregarding time and severity to management location 430. Managementlocation 430 is a location management module, such as managementlocation 310 of FIG. 3.

Timing and severity prediction 426 passes information about the chaoticevent to event requirements 432. Timing and severity prediction 426predicts the severity of the chaotic event in addition to what skillsand resources may be needed as well as the quantities of skills andresources. Event requirements 432 is an event specific module, such asevent requirements 318 of FIG. 3. For example, if an unusually powerfulsolar flare is expected, communications and satellite coordinators andexperts may be required to prevent effects of the solar flare or torecover from the effects after the event.

FIG. 5 is a block diagram for predicting severity of chaotic events inaccordance with the illustrative embodiments. Timing and severityprediction system 500 is a more detailed description of timing andseverity prediction 426 of FIG. 4. As previously described, timing andseverity prediction 502 receives manual input 504.

Timing and severity prediction 502 receives information from catastrophemodels 506. Catastrophe models 506 are models of each possible chaoticevent by region and the resulting affects and consequences of thechaotic event. Catastrophe models 506 are preferably created byscientists and other experts before the occurrence of the chaotic event.For example, catastrophe models 506 may model the effects of a categoryfive hurricane striking South Carolina.

Sensor data 508 is data, such as sensor data 308 of FIG. 3. Additionalinformation resources including, for example, image mapping 510, mapresources 512 and weather information 514 may be used by timing andseverity prediction 502 to determine the severity of the chaotic event.For example, image mapping 510 may show the impact crater of a meteor.Map resources 512 may be used to determine the number of buildingsdestroyed by a tornado. Weather information 514 may be used to showwhether a hurricane is ongoing or whether recovery efforts may begin.Weather information 514 includes forecast models rather than raw data.

Timing and severity prediction 502 uses all available information tomake risk prediction 516. Risk prediction 516 specifies the risksassociated with the chaotic event. For example, risk prediction 516 maypredict the dangers of a magnitude 7.4 earthquake in St. Louis before orafter the earthquake has occurred.

FIG. 6 is a block diagram for finding and organizing skills for chaoticevents in accordance with the illustrative embodiments. Organizationsystem 600 is a system that helps find expert skills or potentiallyavailable skills. Data is collected and organized by data organization602 to populate skills database 604. Skills database 604 is a unifieddatabase of skills and supporting data in discrete and textual form. Forexample, skills database 604 may be implemented in event type skills 320of FIG. 3. The data organized by data organization 602 may be physicallyinstantiated or federated. In other words, the data may be actuallycopied into a database used by data organization 602 or accessed througha query through a federated database. Federated databases may allowaccess to data that is not easily transferred but provides usefulinformation.

Data organization 602 organizes data from any number of sources asherein described. Data is received from discrete data 606 and semanticdata 608. Discrete data 606 is something that may be entered in adatabase, such as numbers or specific words. Semantic data has to beread in context. A pathology report may be broken up into discrete data606 including temperature, alive or dead. Manual input 610 may becommunicated to discrete data 606. Data organization 602 may use queriesfor discrete and semantic data to find necessary information.

Web crawler and semantic search referred to as crawler and semanticsearch 612 may be used to gather data from any number of sources onInternet 614 that are publicly available. Crawler and semantic search612 may be, Webfountain™, produced by International Business MachinesCorporation or other similar products. For example, crawler and semanticsearch 612 may search licenses 616, school records 618, research papers620, immunization records 622, organizational records, and union records624. For example, crawler and semantic search 612 may discover a largenumber of doctors that have graduated from medical school but do nothave licenses in the state where the chaotic event occurred.

Data organization 602 may further access internal skill bank 626,external skill bank 628, vocabularies 630, and legal and otherrequirements 632. Internal skill bank 626 is a skill bank maintained bydata organization 602 in the event of a chaotic event. External skillbank 628 may be a skill bank maintained by an outside organization orindividual. External skill bank 628 may be intended for emergencysituations or may simply be a skill bank for organizing relevant skillsets in other business, government, or miscellaneous settings.

Feedback from inquiries 634 specifies whether an individual is availableand that another individual should be considered. For example, adrilling engineer may disclose unavailability to assist with a minecollapse.

FIG. 7 is a block diagram for finding and organizing routes for chaoticevents in accordance with the illustrative embodiments. Route system 700may be implemented in optimization routine modules, such as optimizationroutines 324 of FIG. 3. Route system 700 is used to optimize availableskills and resources based on distance, traveling time, capacity of aroute, cost, and value as prioritized by decision makers from eventmanagement 302 of FIG. 3. Route system 700 performs optimizations basedon questions which may include how far away the skills or resources are,how long the skills or resources take to get to the necessary location,and what the capacity is. For example, a truck may have a high capacityto move a team of surgeons if a road is available, but may take eighthours to get to a desired location. A helicopter may be used to quicklymove a nuclear engineer regardless of road conditions. Route system 700may be used to perform optimizations based on event requirements 318 ofFIG. 3.

Data organization 702 organizes information from various resources, andthat information is passed to routes database 704. Routes database 704is a unified database of physical and electronic routes includingdistances and capacity for expert skills and resources and limitingconstraints. Constraints for routes may include availability, volume,cost, capacity, bytes, flights per hour, and trucks per day. Routesdatabase 704 may be used by availability components, such asavailability 332 of FIG. 3 to determine whether expert skills andresources are feasibly accessible by a route either physically orelectronically even if they are available.

Data organization 702 receives information from landline public circuits706. Landline public circuits 706 may include communications lines, suchas telephones, fiber-optics, data lines, and other physical means fortransporting data and information. Data organization 702 also receivesinformation from wireless public circuits 708 which may include wirelessaccess points, cell phone communications, and other publicly availablewireless networks.

Data is received from discrete data 710 and semantic data 712. Manualinput 714 may be communicated to discrete data 710. Crawler and semanticsearch 716 may be used to gather data from any number of sources. Forexample, crawler and semantic search 716 may search commercialtransportation schedules 718 to find tractor trailers, busses, airlines,trains, boats, and other means of commercially available means oftransporting people and resources.

Data organization 702 may receive information from road databases 720for determining which roads may be used to access the geographic regionof the chaotic event. Road databases 720 may also specify which roadsare accessible after the chaotic event. For example, after an earthquakein Salt Lake City, Interstate 15 may not be available because ofoverpass collapses.

Data organization 702 may also receive information from bridges andother potential obstacles 722. Airports and other facilities 724 mayprovide additional information regarding airports and other similarfacilities including status and capacity, such as train stations, docks,and other transportation hubs. For example, a data network may beavailable but only with low bandwidth access.

Data organization 702 also receives information from ground station 726.Ground station 726 is a station located on the earth that is used fortransmitting information to or receiving information from satellite 728or other earth orbiting communication devices. For example, informationregarding ground station 726 and satellite 728 may specify capacity,capability, data rates, and availability. Ground station 726 andsatellite 728 may be used by individuals with expert skills or resourcesto coordinate the response to the chaotic event. For example, in theevent that medical images need to be sent from rural Idaho to New YorkCity, ground station 726 and satellite 728 may need to have availablebandwidth. Data organization 702 may also receive information in theform of manual input 730.

FIG. 8 is a flowchart for managing expert resources during times ofchaos in accordance with the illustrative embodiments. The process ofFIG. 8 may be implemented by an event management system, such as eventmanagement system 300 of FIG. 3. In one example, the process of FIG. 8is implemented by a program application that systematically walks one ormore decision makers through the steps and decisions that need to occurto effectively manage the chaotic event. The program applicationsystematically helps the decision make, develop, and implement astrategy for the chaotic event in a logical sequence based on predefinedsteps and priorities.

The process of FIG. 8 begins by detecting a chaotic event (step 802).The event may be detected by a module, such as event detection 304 ofFIG. 3 and event detection system 400 of FIG. 4.

Next, the process selects an event management location and begins activemanagement (step 804). Step 804 may be performed by a module, such asevent management 302 of FIG. 3. The determination regarding eventmanagement location may be made based on feedback from a module, such asmanagement location 310 of FIG. 3. Active management in step 804 mayinvolve managing the situation by deploying personnel with expert skillsand resources and coordinating relevant communication and recoveryefforts.

Next, the process predicts severity and timing of the chaotic event, andthe expert resources required (step 806). Step 806 may be implemented bya module, such as event requirements 318 of FIG. 3 and timing andseverity prediction system 500 of FIG. 5. If the chaotic event isparticularly severe, additional expert skills and resources may berequired. Expert skills may be further determined using a module, suchas organization system 600 of FIG. 6. For example, if a tsunami occursoff the western coast of the United States, a large number of doctorsand water contamination specialists may be required.

Next, the process verifies the availability and cost of the expertresources (step 807). The process of step 807 may be implemented by amodule, such as availability 319 of FIG. 3. Step 807 ensures that onlypotentially available resources are examined to save time, effort, andprocessing power.

Next, the process optimizes the expert resources (step 808). The processof step 808 may be performed by optimization routines, such asoptimization routines 324 of FIG. 3. The expert resources may beoptimized based on factors, such as requirements and constraints 326,potential skills 328, and enabling resources 330 of FIG. 3.

Next, the process confirms the availability of the expert resources bydirect contact (step 810). The process of step 810 may be implemented bya module, such as verify availability 332 of FIG. 3. Availability may bebased on the schedule, time, and commitments of individual experts orgroups of experts. Availability may also be determined based on routesfor communicating and transporting skills and resources based on asystem, such as route system 700 of FIG. 7.

Next, the process determines whether the expert resources are available(step 812). The determination of step 812 may be based ontransportation, cost, proximity, schedule, and time. For example, if thecost of flying a surgeon from Alaska to New York is impractical, theprocess may need to re-optimize the expert resources. If the expertresources are available, the process returns to step 806. The process ofsteps 806-812 is repeated iteratively to optimize and re-optimize theactive management of the response to the chaotic event in step 804.

As a result, the management of the chaotic event is dynamic and adaptsto changing circumstances. For example, if flooding from a hurricanewashes out roads that were previously used to access staging areas, newroutes for medical personnel and supplies needs to be determined in astep, such as step 810. In addition, water contamination experts andwater testing equipment may be required in greater numbers for acategory five hurricane than for a category two hurricane.

If the process determines the expert sources are not available in step812, the process optimizes expert resources (step 808). In other words,optimized expert resources are further re-optimized based on confirmedavailability in step 812. As a result, the decision makers or eventmanagers may deploy the most appropriate resources to effectively manageeach aspect of the chaotic event.

Thus, illustrative embodiments provide a system, method and computerusable program code for managing a chaotic event. By detecting chaoticevents as soon as possible, effective management of expert skills andresources may be quickly and efficiently managed. By effectivelyoptimizing expert skills and available routes based on availability,severity of the chaotic event, and other resulting factors, lives may besaved, and recovery efforts and the appropriate response may begin moreeffectively.

IV. Optimized Selection of Sub-Projects

This section, Section IV, describes a method for mathematically rigorousoptimized selection of project subdivisions using data and requirementssubject to multidimensional constraints. This section is shown withrespect to the optimized selection of sub-projects for a majorinformation technology project. The following section, Section V,describes the application of this optimized selection technology to thearea of chaotic event management.

IV.1 Background of Optimized Selection of Sub-Projects

Large corporations or other large entities use information technologysystems to manage their operations. An information technology system isa system of data processing systems, applications, data, reports, flows,algorithms, databases, and other infrastructure used to maintain thedata and operations of the organization. A large scale informationtechnology system is not necessarily located in one single physicallocation, but can be situated in many different physical sitesimplemented using numerous physical devices and software components. Alarge scale information technology system can be referred to as a majorinformation technology system.

Major information technology system projects, such as those used bylarge corporations, often fail and some fail disastrously. Failure oftencosts millions of dollars, tens of millions of dollars, or even more inwasted time, manpower, and physical resources. Thus, substantial effortis usually exerted in planning the construction of a major informationtechnology system. Planning construction of a major informationtechnology system, at least in theory, reduces the chances of failure.

Major information technology systems projects are beyond the abilitiesof a single individual to implement alone. Likewise, construction ofmajor information technology system projects can not be viewed as asingle monolithic project due to the vastness and complexity of thesesystem projects. Thus, major information technology system projects areoften constructed in phases using groups of sub-projects. Various groupsof people work to complete each sub-project. As work progresses, thesub-projects are assimilated together in order to create the majorinformation technology system project.

However, even with planning and the use of sub-projects, most majorinformation technology system projects fail or are never completed. Evenif the major information technology system project is implemented, theresulting major information technology system project does not functionoptimally with respect to maximizing the efficiency of the organizationfor which the major information technology system project isconstructed. For example, subsets of the whole major informationtechnology system project may not match data, business requirements,and/or resources in an optional manner. As a result, the organizationsuffers from the inefficiencies of the final major informationtechnology system project. Correcting or adjusting these inefficienciesmay be cost prohibitive due to the fundamental nature of how the majorinformation technology system project was constructed.

The most typical reason for failure or inefficiency of these systemprojects is that the construction of these system projects is approachedfrom a non-data centric viewpoint. Instead, design of sub-projects ofmajor information technology system projects often is performed bymanagers, executives, or others who are experts at understanding where abusiness should go or how a business should operate, but are nottechnically proficient at implementing or constructing a majorinformation technology system project. As a result, the sub-projects“look good on paper” but, when implemented, fail or, if successfulindividually, can not be integrated together in a desired manner. Anentire major information technology system project may fail or beinefficient if sub-projects that were designed to build the majorinformation technology system projects can not be integrated. Currentlyavailable methods and system projects do not provide a means to reliablycreate efficient major information technology system projects.Therefore, it would be advantageous to have an improved method andapparatus for creating optimized sub-projects useful for creating andimplementing a major information technology project.

IV.2 Optimized Selection of Sub-Projects for a Major InformationTechnology Project: Definitions and Examples

A computer-implemented method, computer program product, and dataprocessing system are provided for creating an optimized majorinformation technology project having optimally selected optimizedsub-projects. An optimized sub-project is a set of data representing aportion of the project. For example, an optimized sub-project could be aset of data that describes how physical data processing systems shouldbe setup relative to each other. Another example of an optimizedsub-project could be a set of data that describes how business reportsshould be generated, what information should be included in the businessreports, who should receive the business reports and the order in whichthe business reports should be generated. Many other examples ofoptimized sub-projects exist.

As part of an exemplary process, one or more data processing systemsreceive boundary conditions, input regarding output objects, and inputregarding “as-is” data sources. An “as-is” data source is an existingdata source. Boundary conditions include all data that places one ormore boundaries on a project. Examples of boundary conditions includeresource data and constraint data. Resource data reflects resourcesavailable for the project, such as money and manpower. Constraint dataincludes constraints imposed on the project, such as data reflectingdeadlines, legal requirements, data availability, and others. A specialtype of constraint data is data regarding political concerns. Datareflecting political concerns includes data that reflects politicalrealities, such as resource allocation among organizational departments,timing of deliverables, and work allocation.

Examples of input regarding output objects include data reflective ofoutput objects. Output objects are those outputs or deliverables thatthe project is designed to deliver. Specific examples of output objectsinclude screens showing particular information, pictures, or queryresults; interactive graphical user interfaces; reports; servicesdelivered, including deliverables; applications; queries; applications,flows, and algorithms; combinations thereof, and others.

Data regarding “as-is” data sources is data reflective of available datasources. Examples of “as-is” data sources can include availabledatabases, available files, available hard-copy paper files, and otherdata sources. An “as-is” data source is not a data source that is yet tobe developed or that is yet to be placed into a desired form. An “as-is”data source is distinguished from a “to be” data source. A “to be” datasource is a data source that is not yet in existence or is not yet in adesired form, but that has been modeled or can be modeled.

Once the boundary conditions, input regarding output objects, and inputregarding “as-is” data sources are received, the output objects aredecomposed into data objects. A data object is a data structure thatcontains data reflective of an output object. A data object can be an“object” as that term is used in object-oriented programming forcomputer languages such as C++ and Java.

The term “decompose,” which also includes the concept of factoring incomputer science, refers to the process by which a complex problem orsystem is broken down into parts that are easier to conceive,understand, program, and maintain. In structured programming,algorithmic decomposition breaks a process down into well-defined steps.In object-oriented programming, one breaks a large system down intoprogressively smaller classes or objects that are responsible for somepart of the problem domain. An object, process, data, or flow can be“decomposed” in a mathematical, data-centric manner according to manyknown methods.

Additionally, the output objects are also decomposed into process dataobjects, which are data objects reflective of logical processes used tocreate the output objects. A logical process used to create an outputobject can be any application, flow, algorithm, or similar process forcreating an output object. Such flows can also be characterized as“objects” as that term is used in object-oriented programming.

The illustrative examples also include determining value clusters. Avalue cluster is the discrete intersection of data and that data'sability to add value to an organization. A value cluster may also beconsidered a group of resources that, when taken together, support oneor more output objects having a utility value to an organization. Avalue cluster may be considered one of a data value cluster and aprocess value cluster. A data value cluster includes one or more datasources that support one or more data objects. A process value clusterincludes one or more logical processes that support one or more outputobjects.

Next, the data objects are organized into “to be” data structures toform a “to be” data model and the “to be” data structures are mapped tothe “as-is” data sources. The phrase “to be”, as used herein, describessomething that is desired for the major information technology projectthat may, or may not, yet exist. The phrase “to be” also can be referredto as “future” or “future model.” Thus, a “to be” data model is a modelor other description of a future data model. A “to be” data structure isdata assembled into data models appropriate to producing one or moreoutput objects. A “to be” data structure therefore includes one or moredata objects, as defined above. Different parts of the “to be” datamodel may be at different levels of completeness. An example of a “tobe” data structure is a data structure that shows the “skeleton” of amassive database that is to be constructed. Although not all informationregarding the future database is available, the “to be” model of thedatabase describes the structure of the database and what information itshould contain.

Continuing with the illustrative example, the “to be” data structuresare mapped to the “as-is” data sources. An “as-is” data source is anexisting source of data. The existing source of data may not becomplete, may not be of sufficient quality, and may not be in a formatdesired for the completed project; nevertheless, the “as-is” datasources may be adequate for the completed project.

Next, additional processes are determined for moving data from a sourceto a target. For example, a process can be determined for summarizingraw patient data and turning that raw patient data into a report for ahospital executive or for a doctor. Additionally, transformation issuesare incorporated into the processes, such as estimating the costs andrisks of moving data from a source to a destination in the correctformat.

The exemplary embodiment also includes creating an affinity matrix basedon the value clusters. An affinity matrix is a matrix of data thatindicates a relationship between groups of data sources and groups ofoutput objects, and/or groups of available logical processes and groupsof output objects. The affinity matrix describes data sources and outputobjects in terms of what output objects are available based on what datasources are available. Thus, for example, the affinity matrix can allowa user to determine that if Output Object “X” is available because itsdata sources are available, then Output Object “Y” and Output Object “Z”are also available because they use similar data sources.

Finally, an optimization operation is executed with an optimizationengine to produce the optimized sub-projects. The optimization enginetakes as inputs the boundary conditions, the “as-is” data sources, thedata objects, the logical processes used to create the output objects,the value clusters, the “to be” data structures; the mapping of the “tobe” data structures to the “as-is” data sources, the additionalprocesses for moving data from the source to the target, and theaffinity matrix.

Optimization, as used herein, is the mathematical study of problems inwhich a minimum or a maximum for a function of a real variable is soughtby systematically choosing the values of the real number or integervariables from within an allowed set. The problem can be mathematicallyrepresented as follows:

Given: A function f: A-R from some set A to the real numbers. Sought: Anelement x₀ in A such that f(x₀)≦f(x) for all x in A (“minimization”) orsuch that f(x₀)≧f(x) for all x in A (“maximization”).

Typically, A is some subset of the Euclidean space Rn, often specifiedby a set of constraints, equalities or inequalities that the members ofA have to satisfy. The elements of A are called feasible solutions. Thefunction f is called an objective function, or cost function. A feasiblesolution that minimizes or maximizes the objective function is called anoptimal solution. The domain A of f is called the search space, whilethe elements of A are called candidate solutions or feasible solutions.

Generally, when the feasible region or the objective function of theproblem does not present convexity, there may be several local minimaand maxima, where a local minimum x* is defined as a point for whichthere exists some δ>0 so that for all x such that∥x−x*∥≦δ;

the expressionƒ(x*)≦ƒ(x)

holds. In other words on some region around x* all of the functionvalues are greater than or equal to the value at that point. Localmaxima are defined similarly.

Commercial optimization engines are available and can be used with theillustrative examples described herein. Examples of commercialoptimization engines include Optimization Subroutine Library and MPSX(Mathematical Programming System Extended), both available fromInternational Business Machines Corporation, ILOG Cplex, and GLPK (GnuLinear Programming Kit). Thus, as defined herein, the term “optimizedsub-project” refers to a mathematically defined data structure thatdescribes the structure of a sub-project and steps to be taken toimplement a sub-project of a major project. Accordingly, describeddifferently, the exemplary processes described herein provide a computerimplemented method, apparatus, and computer usable program code forgenerating optimized sub-projects based on a weighted value of desiredoutputs mapped against source data, required transformations,boundaries, and an affinity matrix.

The project sought to be constructed using the illustrative embodimentsdescribed herein can be any large project. Examples of other largeprojects suitable for the planning techniques described herein includegovernment agencies, outer-space programs, major military operations,and other major projects. However, the non-limiting embodimentsdescribed herein provide an illustrative example of creating a majorinformation technology project.

Taken together, the group of optimized sub-projects can be assimilatedinto a plan an organization can follow to build the most efficientproject plan possible. Because the plan is data-centric, an efficientmajor information technology project can be constructed even if themajor information technology project is very large and complex.

IV.3 Mathematically Optimized Selection of Subprojects for MajorInformation Technology Projects

The following figures describe in detail the problem to be solved, theinadequacies of the prior art, and examples of the solution to theproblem to be solved. FIG. 9 through FIG. 12 describe the problem to besolved and the inadequacies of the prior art. FIG. 13 through FIG. 15illustrate a summary of an illustrative embodiment for solving theproblem described in FIG. 9 through FIG. 12. FIG. 16 through FIG. 30provide a detailed description of the devices and methods useful forimplementing the illustrative embodiments described herein. FIG. 31provides another overview of an illustrative embodiment for solving theproblem of planning a project. FIG. 32 is a flowchart illustrating anillustrative embodiment of planning a major information technologyproject.

As specified above, FIG. 9 through FIG. 12 describe the problem to besolved and the inadequacies of the prior art. Common reference numeralsused in different figures correspond to each other. Thus, for example,major information technology project 902 is the same in FIG. 9, FIG. 10,FIG. 11, and FIG. 12.

Referring now to the particular figures, FIG. 9 is a block diagramillustrating a major information technology project, in accordance withan illustrative embodiment. An organization has organizational goal 900that the organization desires to implement. The goal may be to create ahospital system, a new government agency, a new major corporation, orany other goal. In the illustrative embodiments described herein,organizational goal 900 is a large scale goal similar to those describedin the previous examples.

As part of organizational goal 900, major information technology project902 is to be implemented to create a large scale information technologysystem project. An information technology system project is a system ofdata processing systems, applications, data, reports, flows, algorithms,databases, and other infrastructure used to maintain the data andoperations of the organization. A large scale information technologysystem project is not necessarily located in one single physicallocation, but can be situated in many different physical sitesimplemented using numerous physical devices and software components. Alarge scale information technology system project can be referred to asa major information technology system project.

Major information technology project 902 has as goals one or more outputobjects. Output objects are those outputs or deliverables that theproject is designed to deliver. Specific examples of output objectsinclude report 904, report 906, application 908, application 910, screen912, and screen 914.

A report is any type of output of a query or process. For example, areport could be a list of the number of patients having a particular setof properties. A report could also be a quarterly financial statement orany other type of report as that word is commonly known in business.

An application is any type of software application. An application canalso be a script, flow, or other process that can be implemented in acomputer.

A screen can be any graphical user output of an application. A screencan be a graphical user interface adapted to accept user input. Forexample, a screen could be a graphical user interface adapted to accepta query for a database, or a screen could be a graphical user interfaceadapted to accept data for entry into a database. A screen can alsodisplay a report.

Although major information technology project 902 is expressed as havingoutput objects 904, 906, 908, 910, 912, and 914, many other types ofoutput objects could also exist. For example, other types of outputobjects could be application, database, data cube, data structure, flatfile of data, a graph, a directed graph, a project plan, an automatedcontrol system, a virtual reality visualization, a printed report, anon-screen representation of a printed report, a Web page, an email, anXML (Extended Markup Language) data structure, a document, a submissionfor an organization such as a government agency (like a FDA submission),an alert, a natural language representation of data, and a notificationlist. Other types of output objects exist.

FIG. 10 is a block diagram of a prior art method of constructing a majorinformation technology project. The method shown in FIG. 10 can beimplemented in one or more data processing systems, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. The method shown in FIG. 10 canbe implemented among multiple computers over a network, such as network102 shown in FIG. 1.

Once an organization has specified organization goal 900 and the outputobjects desired for major information technology project 902 of FIG. 9,the organization then has to cause major information technology project902 to be created. In the prior art, the process of implementing majorinformation technology project 902 is performed in a “left to right”manner.

Specifically, one or more individuals identify all resources available1000 to the organization for major information technology project 902.Examples of resources are shown in FIG. 27, though can include itemssuch as money, manpower, existing databases, existing software, and thelike. Similarly, one or more individuals identify all constraints 1002imposed on the organization for major information technology project1002. Examples of constraints are shown in FIG. 28, though can includeitems such as legal constraints, security requirements, timeconstraints, and the like.

Resources 1000 and constraints 1000 are fed into “as-is” model 1004. An“as-is” model describes all of the identified resources available tomajor information technology project 902 and all of the identifiedconstraints imposed on major information technology project 902. Thus,an “as-is” model can be referred to as an existing model that describesexisting resources. An “as-is” model can be reflected in a database orsome other computer readable format; however, often “as-is” model 1004is an ad-hoc report used by individuals to manually define subprojects1006. Thus, one or more individuals and/or one or more computer programsdefine subprojects 1006. The sub-projects are then individually executedin a specified order, some of which are performed in parallel, toimplement major information technology project 902.

FIG. 11 is a block diagram of a set of sub-projects created using theprior art method shown in FIG. 10. As a result of performing the step of“define sub-projects” 1006 in FIG. 10, major information technologyproject 902 is divided up into sub-projects as shown. In theillustrative example of FIG. 11, major information technology project902 includes six sub-projects; sub-project 1102, sub-project 1104,sub-project 1106, sub-project 1108, sub-project 1110, and sub-project1112. Each sub-project is shown as having various different areas on thefigure in order to show that each sub-project can have a different scalein terms of difficulty, size, or some other parameter. Most sub-projectsare performed in a particular order, though some sub-projects could beperformed in parallel.

Each sub-project reflects a particular aspect of building majorinformation technology project 902. In a non-limiting example, eachsub-project has a particular purpose described as follows. Sub-project1102 is a sub-project to implement the physical machinery and wiringused to implement major information technology project 902. Sub-project1104 is a project to create a new database used in major informationtechnology project 902. Sub-project 1106 is a project to create a newsoftware application useful for performing temporal analysis on data.Sub-project 1108 is a project to convert existing data to a new format.Sub-project 1110 is a project to create a graphical user interface forinteracting with the database to be defined in sub-project 1104.Sub-project 1112 is a project to develop a second database.

Although sub-projects 1102 through 1112 are described in terms ofspecific examples, many other types of sub-projects exit. Additionally,major information technology project 902 can include more or fewersub-projects. Most major information technology project 902 would havemany more sub-projects. Moreover, sub-projects 1102 through 1112 couldeach include one or more smaller sub-projects. Each smaller sub-projectis used to plan construction of the corresponding larger sub-project.Conceivably, smaller sub-projects could also include deeper levels ofsub-projects.

FIG. 12 is a block diagram illustrating major information technologysub-projects that inefficiently overlap underlying realities of existinginformation technology systems as a result of the prior art method shownin FIG. 10. As stated previously, the prior art method show in FIG. 10of generating sub-projects for major information technology project 902is unsatisfactory. The prior art method shown in FIG. 10 isunsatisfactory because the prior art method often results in totalfailure of major information technology project 902 or results in afinal major information technology project that has unacceptableinefficiencies.

The cause of this result is illustrated in FIG. 12. In broad terms, theprior art method shown in FIG. 10 does not take into account theunderlying technical realities of existing systems in “as-is” model1004. In other words, the prior art method shown in FIG. 10 is not datacentric. A method of creating a major information technology project isdata centric when the method is based on empirical data, even if theempirical data includes subjective considerations that have been reducedto data models.

For example, major information technology project 902 shows sub-project1106 and sub-project 1110 as defined according to the method shown inFIG. 10. However, each of sub-project 1106 and sub-project 1110 overlapmultiple “as-is” conceptual objects. An “as-is” conceptual object issome underlying physical information technology-related thing. An“as-is” conceptual object can be an existing conceptual object. Forexample, sub-project 1110 overlaps all three of “as-is” data structure1200, “as-is” data structure 1202, and “as-is” data structure 1204.Similarly, sub-project 1106 overlaps both “as-is” data structure 1200and “as-is” data structure 1204. Although blocks 1200, 1202, and 1204are characterized as “as-is” data structures, one or more of theseblocks could be replaced with “as-is” applications, databases, physicalhardware, or other “as-is” conceptual objects.

The overlap of sub-projects to multiple “as-is” conceptual objects shownin FIG. 12 illustrates why the prior art method shown in FIG. 10 oftenfails. Because sub-projects are designed without taking into account theunderlying “as-is” conceptual objects, work on sub-projects proceedswithout having all pertinent information. Those working on sub-project1106 do not appreciate that “as-is” data structure 1200 will impactconstruction of both sub-project 1106 and sub-project 1110. As a result,duplicative effort may take place, resulting in possibly grossinefficiency. Alternatively, “as-is” data structure is not modified tohandle the workload imposed by both sub-project 1106 and sub-project1110, resulting in failure of both projects.

As stated above, FIG. 13 through FIG. 15 illustrate a summary of anillustrative embodiment for solving the problem described in FIG. 9through FIG. 12. Common reference numerals used in different figurescorrespond to each other. Thus, for example, major informationtechnology project 902 is the same in FIG. 9 through FIG. 15.

In particular, FIG. 13 is a block diagram illustrating acomputer-implemented method of creating optimized sub-projects for amajor information technology project, in accordance with an illustrativeembodiment. The method shown in FIG. 13 can be implemented in one ormore data processing systems, such as data processing systems 104, 106,110, 112, and 114 in FIG. 1 or data processing system 200 shown in FIG.2. The method shown in FIG. 13 can be implemented among multiplecomputers over a network, such as network 102 shown in FIG. 1.

FIG. 13 illustrates a counter-intuitive method 13000 of selecting a setof optimized sub-projects into a plan for creating an optimal projectdefinition. Instead of proceeding from a “right to left” perspectiveshown in FIG. 10, the illustrative embodiment shown in FIG. 13 solvesthe problem of planning major information technology problem 902 from“left to right.” Specifically, the term “left to right” in this contextmeans that the illustrative process first defines a solution model 1302,instead of defining the problem—as in FIG. 10.

The solution model 1302, resources 1000, constraints 1002, and politicalconcerns 1306 are all described in terms of data that can be manipulatedby a computer-implemented process. Thus, the definition of solutionmodel 1302, resources 1000, constraints 1002, and political concerns1306 are provided to optimization engine 1304. An optimization engine isa computer-implementable software application that performs rigorouslydefined mathematically optimization, as defined above, on inputs 1302,1306, 1000, and 1002.

After feedback 1308, the output of optimization engine is an optimizedmajor information technology project 902 having optimally selectedsub-projects 1310. The term “optimally selected sub-projects” means thatthe sub-projects were selected via a mathematical optimization project.

The process, however, usually proceeds through several adjustments anditerations in order to bring the optimized major information technologyproject 902 into closer agreement with expectations of those responsiblefor major information technology project 902. Thus, feedback process1308 allows a user or process to adjust one or more of solution model1302, resources 1000, constraints 1002, or political concerns 1306 andthen re-execute optimization engine 1304.

Ultimately, the result of the process shown in FIG. 13 is an optimizedmajor information technology project 902 having optimally selectedsub-projects 1310 that are in accord with expectations of thoseresponsible for major information technology project 902. The processshown in FIG. 13 is data centric. In other words, the process shown inFIG. 13 is based on data and mathematical characterizations of factorsimportant to major information technology project 902. As a result, asshown in FIG. 14 and FIG. 15, the optimally selected sub-projects 1310more closely reflect underlying realities of “as-is” conceptual objects.Thus, by using the method shown in FIG. 13, the probability of successof completing an efficient major information technology project 902 isgreatly increased.

FIG. 14 is a block diagram illustrating optimally selected sub-projectsfor a major information technology project, in accordance with anillustrative embodiment. The optimally selected sub-projects shown inFIG. 14 are different than the non-optimally selected sub-projects shownin FIG. 12. Thus the shapes of sub-project 1400, sub-project 1402,sub-project 1404, sub-project 1406, sub-project 1408, sub-project 1410,and sub-project 1412 are different than the various sub-projects shownin FIG. 12. Optimally selected sub-projects shown in FIG. 14 are part ofmajor information technology project 902.

By implementing optimally selected sub-projects 1400 through 1412 in aparticular order, which could be parallel implementation in someinstances, the probability of successfully implementing majorinformation technology project 902 is substantially increased.

FIG. 15 is a block diagram illustrating major information technologysub-projects that efficiently overlap underlying realities of existinginformation technology systems, in accordance with an illustrativeembodiment. FIG. 15 illustrates why the process shown in FIG. 13 issuperior to the prior art method shown in FIG. 9.

Unlike in FIG. 12, which is a result of the prior art method shown inFIG. 10, the shown optimally selected sub-projects directly overlapunderlying conceptual data objects. For example, optimally selectedsub-project 1400 corresponds directly to conceptual data object 1200without overlapping conceptual data object 1202. Similarly, optimallyselected sub-project 1402 directly corresponds to conceptual data object1202 without overlapping conceptual data object 1200. Thus, duplicativeeffort is avoided and major information technology project 902 is muchmore efficiently produced. Additionally, major information technologyproject 902 operates more efficiently when completed.

FIG. 16 is a block diagram illustrating a method of creating optimizedsub-projects for a major information technology project, in accordancewith an illustrative embodiment. In particular, the method shown in FIG.16 is a more detailed version of the method shown in FIG. 13. Thus,corresponding reference numerals shown in FIG. 16 correspond to likenumerals shown in FIG. 13. The method shown in FIG. 16 can beimplemented in one or more data processing systems, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. The method shown in FIG. 16 canbe implemented among multiple computers over a network, such as network102 shown in FIG. 1.

As in FIG. 13, solution model 1302, resources 1000, constraints 1002,and political concerns 1306 are provided to optimization engine 1304. Inconjunction with optional feedback 1308, optimization engine createsoptimized major information technology project 902 having optimallyselected sub-projects 1310. However, the method shown in FIG. 16 detailssolution model 1302.

Construction of solution model 1302 begins with creating output objectdefinitions 1602. Output objects are those outputs or deliverables thatthe project is designed to deliver. Specific examples of output objectsinclude screen shots showing particular information, pictures, or queryresults; reports; services delivered; applications; queries; and others.Output objects are decomposed into three types of conceptual dataobjects: output data objects, process data objects, and connector dataobjects that connect the former two data objects. Output data objectsare data objects that represent data and data structures, such asdatabases and other similar data objects. Process data objects are dataobjects that represent processes used to create the output objects, suchas applications, algorithms, and flows.

For example, an output object could be a report. Decomposing this outputobject results in a corresponding output data object that is data thatidentifies or represents the report. Additionally, the report isdecomposed into the logical processes used to create the report.Continuing the example, the each of three applications, algorithms, orflows used to create the report are identified and represented as a dataobject that can be called an output process object.

The sum of decomposed output data objects are then collected andassimilated into “to be” data model 1604. “To-be” data model 1604 is amodel of all output data objects, data structures desired or needed forthe output data objects, and any other data objects desired to implementoptimized major information technology project 902. “To be” data model1604 is data assembled into data models appropriate to producing one ormore optimized sub-projects. A “to be” data structure therefore includesone or more data objects, as defined above.

Different parts of “to be” data model 1604 may be at different levels ofcompleteness. Thus, an example of “to be” data model 1604 is a datastructure that shows the “skeleton” of a massive database that is to beconstructed. Although not all information regarding the future databaseis available, the “to be” model of the database describes the structureof the database and what information it should contain.

Similarly, the sum of decomposed output process objects are collectedand assimilated into “to be” process model 1606. “To-be” process model1606 is a model of processes and flows desired to implement optimizedmajor information technology project 902. “To be” process model 1606 isdata assembled into data models appropriate to producing one or moreoptimized sub-projects. A “to be” process therefore includes one or moredata objects, as defined above.

Different parts of “to be” process model 1606 may be at different levelsof completeness. Thus, an example of “to be” data model 1606 is anapplication that has not yet been written or that is incomplete.Although not all information regarding the future application isavailable, the “to be” model of the application describes the structureof the application and what information it should contain.

Next, “to be” data model 1604 and “to be” process model 1606 arearranged into value clusters. Possibly, connector data objects generatedduring output object definition 1602 are also integrated into valueclusters 1608. A value cluster is the discrete intersection of data andthat data's ability to add value to an organization. A value cluster mayalso be considered a group of resources that, when taken together,support one or more output objects having a utility value to anorganization.

Value clusters 1608 may be considered a group of data value clusters,process value clusters, and connector value clusters. Each data valuecluster includes one or more data sources that support one or more dataobjects. Each process value cluster includes one or more logicalprocesses that support one or more output objects.

Value clusters 1608 are then related to each other using affinity matrix1610. Affinity matrix 1610 is a matrix of data that indicates arelationship between groups of data sources and groups of outputobjects, and/or groups of available logical processes and groups ofoutput objects. The affinity matrix describes data sources and outputobjects in terms of what output objects are available based on what datasources are available. Thus, for example, the affinity matrix can allowa user to determine that if Output Object “X” is available because itsdata sources are available, then Output Object “Y” and Output Object “Z”are also available because they use similar data sources.

Values clusters 1608, through affinity matrix 1610 are provided tooptimization engine 1604. Optimization engine 1604 then performsmathematical optimization operations, taking as input affinity matrix1610.

Returning to “to be” data model 1604, additional considerations aretaken into account. For example, “to-be” data structures are mapped to“as-is” data structures, taking as input data source 1614. An “as-is”data structure is an existing data structure. Often, “as-is” data ordata structures are not in a format compatible with final optimizedmajor information technology project 902. Thus, the mapping of “to-be”data and data structures to “as-is” data and data structures ischaracterized as a conceptual data object.

Together with source data quality scoring 1616, mapping 1612 is modeledaccording to transformation issues 1618. Transformation issues 1618 arerigorously defined transformation risks and problems involved withmapping 1612 “to be” data and data structures to “as-is” data and datastructures. Examples of transformation issues 1618 include estimatedcosts for source to target conversion, estimated risk for source totarget conversion, and other similar issues.

As described above, when considered as a whole output object definition1602, “to be” data model 1604, “to be” process model 1606, valueclusters 1608, affinity matrix 1610, “to be” to “as is” mapping 1612,source data 1614, source data quality scoring 1616, and transformationissues 1618 form solution model 1302. Solution model 1302 is provided asinput into optimization engine 1304, along with resources 1000,constraints, 1002, political concerns 1306, and feedback 1308. As aresult of performing optimization, a deterministic optimized majorinformation technology project 902 is produced with optimally selectedsub-projects 1310.

FIGS. 17 through 31 illustrate various components and aspects of thefeatures of FIG. 16. Thus, corresponding reference numerals in thedifferent figures refer to the same features.

FIG. 17 is an exemplary output object valuation matrix, in accordancewith an illustrative embodiment. An output object valuation matrixreflects valuation data, which is data that describes the value of anoutput object or a resource to an organization. An output objectvaluation matrix can be implemented as data and a data structure usableby a data processing system, such as data processing systems 104, 106,110, 112, and 114 in FIG. 1 or data processing system 200 shown in FIG.2. Exemplary output object valuation matrix 1700 shown in FIG. 17 can beimplemented among multiple computers over a network, such as network 102shown in FIG. 1. Additionally, output object valuation matrix 1700 shownin FIG. 17 describes the value of various projects, such as optimizedsub-projects 1400 through 1412, to an organization. Output objectvaluation matrix 1700 is useful for determining affinity matrix 1610 inFIG. 16 and can also be used as input in optimization engine 1304.

As shown in FIG. 17, rows 1702 reflect various sub-organizations withinthe overall organization. Columns 1704 reflect projects. Differentsub-organizations within the organization can value different projectsdifferently. Output object valuation matrix takes these differentvaluations into account when creating an optimized major informationtechnology project for which optimized sub-projects are selected.

Columns 1704 include information technology sub-project 1706, managementsub-organization 1708, marketing sub-organization 1710, and productionsub-organization 1712. Additional sub-organizations or differentsub-organizations could exist.

Columns include project 1 1714 and project 2 1716. Examples of projectscould include an optimized sub-project, as described above. Additionalprojects or a different number of sub-projects could exist. A specificexample of a project could include establishing a database, creating anapplication, generating a graphical user interface, or any otherproject.

An intersection of a row and a column can be referred to as a cell. Eachcell contains a real number. The real number is a relative valuation ofa project to an organization. High numbers reflect greater importance.Low numbers reflect lower importance. A zero indicates that a projecthas no importance to the organization. A negative number indicates thata project is a detriment to the organization.

For example, project 1 1714 has a value of 100 to managementsub-organization 1708 and project 2 1716 has a value of 400 tomanagement sub-organization 1708. Thus, project 2 1716 is considered tobe much more important to the management sub-organization 1708 thanproject 1 1714. Similarly, both project 1 1714 and project 2 1716 aremore important to management sub-organization 1708 than to the othersub-organizations.

In turn, project 1 1714 has no value to marketing sub-organization 1710.For example, project 1 could be creation of a database with whichmarketing sub-organization 1710 does not interact.

However, project 1 1714 has a negative value to productionsub-organization 1712. For example, project 1 1714 could interfere withoperation of production sub-organization 1712 because project 1 1714drains production sub-organization 1712 of resources needed by thatorganization. This fact could motivate a change in project 1, a changein production sub-organization 1712, or a change in some other part ofmajor information technology project 302 shown in FIG. 9.

FIG. 18 is a block diagram of a “to be” data model, in accordance withan illustrative embodiment. “To be” data model 1800 corresponds to “tobe” data model 1604 in FIG. 16.

A “to be” data model includes a group of “to be” data structures and “tobe” data. The phrase “to be”, as used herein, describes something thatis desired for the major information technology project that may, or maynot, yet exist. A “to be” data structure is data assembled into datamodels appropriate to producing one or more output objects. A “to be”data structure therefore includes one or more data objects, as definedabove. Different parts of the “to be” data model may be at differentlevels of completeness. An example of a “to be” data structure is a datastructure that shows the “skeleton” of a massive database that is to beconstructed. Although not all information regarding the future databaseis available, the “to be” model of the database describes the structureof the database and what information it should contain.

Examples of “to be” data structures in “to be” data model 1800 includereport data structure 1802, screen data structure 1804, productionschedule data structure 1806, deliverable data structure 1808, databasedata structure 1810, and file data structure 1812. Additional “to be”data structures or different “to be” data structures could be includedin “to be” data model 1800.

Report data structure 1802 could be a data structure detailing data ordata structures desired, whether available or not, for a report outputobject. Similarly, screen data structure 1804 could be a data structuredetailing data or data structures desired, whether available or not, fora screen output object. Likewise, production schedule data structure1804 could be a data structure detailing data or data structuresdesired, whether available or not, for a production schedule outputobject. Likewise, deliverable data structure 1806 could be a datastructure detailing data or data structures desired, whether availableor not, for a deliverable output object. Likewise, database datastructure 1810 could be a data structure detailing data or datastructures desired, whether available or not, for a database outputobject. Finally, file data structure 1812 could be a data structuredetailing data or data structures desired, whether available or not, fora file output object.

Taken together, the set of all “to be” data structures, and possiblyrelationships among the “to be” data structures, form “to be” data model1800. “To be” data model 1800 will then be used as shown in FIG. 16.

FIG. 19 is a block diagram of a “to be” process model, in accordancewith an illustrative embodiment. “To be” data model 130 corresponds to“to be” data model 1606 in FIG. 16.

A “to be” process model includes a group of “to be” data structures and“to be” data reflective of a process used to implement an output object.The phrase “to be”, as used herein, describes something that is desiredfor the major information technology project that may, or may not, yetexist. Different parts of the “to be” process model may be at differentlevels of completeness. An example of a “to be” data structure in a “tobe” process model is a data structure that describes a massiveapplication to be used in the major information technology project.Although not all information regarding the future application isavailable, the “to be” model of the application describes the structureof the application and what capabilities it should have.

Examples of “to be” data structures in “to be” process model 1900include application data structure 1902, query data structure 1904, flowdata structure 1906, and algorithm 1908. Additional “to be” datastructures or different “to be” data structures could be included in “tobe” process model 1900.

Application data structure 1902 could be a data structure detailing dataor data structures desired, whether available or not, for an applicationoutput object. Similarly, query data structure 1904 could be a datastructure detailing data or data structures desired, whether availableor not, for a query output object. Likewise, flow data structure 1906could be a data structure detailing data or data structures desired,whether available or not, for a flow output object. Finally, algorithmdata structure 1908 could be a data structure detailing data or datastructures desired, whether available or not, for an algorithm outputobject.

Taken together, the set of all “to be” process data structures, andpossibly relationships among the “to be” process data structures, form“to be” process model 1900. “To be” process model 1900 will then be usedas shown in FIG. 16.

FIG. 20 is a block diagram illustrating data value clusters, inaccordance with an illustrative embodiment. The process of forming datavalue clusters can be implemented using a data processing system, suchas data processing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. Data value clusters can beimplemented among multiple computers over a network, such as network 102shown in FIG. 1. As described above, a data value cluster is thediscrete intersection of data and that data's ability to add value to anorganization.

To form data value clusters, data structure models from “to be” datamodel 1800 are associated with different common data sources in datavalue clusters. For example, data value cluster 2000 includes datasource 2002 and data source 2004. Report data structure model 1802 andscreen data structure model 1804 each take advantage of these datasources in data value cluster 2000. Report data structure model 1802also takes advantage of data value cluster 2006, which contains datasource 2002, data source 2004, and data source 2008. Report datastructure model 1802 also takes advantage of data value cluster 2010,which includes data source 2002, data source 2004, data source 2008, anddata source 2012. Different data structure models are associated withdifferent data value clusters as shown.

Structurally, data value clusters are implemented using matrices. Eachvalue cluster is analyzed for its political and economic value to anorganization or activities of an organization. Political power isexpressed as a floating point number between 0.0 and positive infinity.Zero is totally powerless and positive infinity is an organization thatreceives anything it requests, if within the power of the organization.Example corporate organizations with a political power of infinity arethe audit, compliance, and Sarbanes-Oxley compliance organizations.Other legal, tax, and environmental laws must be complied with, so notradeoffs exist versus normal organizational goals. Most normalorganizations would have a scaled political power value between 0.0 and1.0. The absolute values of political power do not matter, only thepolitical power ratio effects value cluster selection.

For example, relative political power can be objectively quantified indata value clusters using the following mathematics. Initially, anorganization index is produced, where,0≦PP_(X=1) ^(N)≦∞Where X is the organizational index from 1 to N organizations.

Each of the value clusters provides some non-negative value to each ofthe X organizations. For the C value clusters projects considered by theplanning unit, the value of each proposed cluster is:0≦VC_(X,C)Where X is the organization index and C is the index of the valuecluster, and VC is the value cluster.

For each value cluster there exists a cost to extract, transform, andload the data to provision a particular value cluster. That cost is anon-negative number, and the cost of each value cluster is less than orequal to the total budget available, as shown by the followinginequality:0≦Cost_(D=1) ^(C)≦BudgetWhere C is the total number of value clusters and Budget is the totalbudget available.

The optimal value cluster selection is a binary vector composed of zerosand ones that indicates the selection and funding of particularprojects. This binary vector can be considered the “answer”. As theoptimization algorithm is run, various combinations of value clustersare computed in an attempt to increase the total value of an objectivefunction described below. This process is well known in the art.Plan_(γ=1) ^(C)ε0,1Where Plan sub Y is 1 if production of the corresponding value clusteris selected and zero otherwise.

The methods described herein maximize the total value of the selectedvalue clusters for the entire organization subject to a series ofconstraints. The objective function that is maximized is:${Maximize}\quad{\sum\limits_{X = 1}^{M}\quad{\sum\limits_{Y = 1}^{C}\quad{{PP}_{X} \cdot {Plan}_{Y} \cdot {VC}_{X,Y}}}}$Where M=maximum organizational index and C=number of value clusters.

The production of a given value cluster may deliver value to more thanone part of the organization. For instance, a new production planningsystem may deliver value to a manufacturing department by improvingmanufacturing efficiency. This same value cluster may also deliver valueto the marketing department by allowing sales persons to know when aparticular order for a given customer will ship. The shipping departmentmay also receive value by being able to negotiate favorable shippingrates by more accurate prediction of shipping needs.

Constraint data are added to reflect various constraints on theorganization, such as physical, financial, organizational, legal,ethical, staffing, infrastructure, scheduling, and operationalrealities. For example, the total costs for all selected value clusterprojects is less than or equal to the total budget available, asreflected in the following equation:

-   -   Subject to:        $0 \leq {\sum\limits_{Y = 1}^{C}\quad{{PP}_{Y} \cdot {Cost}_{Y}}} \leq {Budget}$        Where Cost sub Y is the cost of producing value cluster number        j.

Other constraints may be added to reflect the managerial or politicalconsiderations of the organization. For instance, if everyorganizational unit must receive at least 5% of their requested clustervalues, a possible constraint would be:

For all dept,$\left( {\sum\limits_{P = 1}^{C}\quad{{.05}*{VC}_{X,P}}} \right) \leq \left( {\sum\limits_{Q = 1}^{C}\quad{{Plan}_{Q}*{VC}_{X,Q}}} \right)$Where X=department index, Q=project index, and Plan sub Q is the valuecluster selection vector.

Other mathematical constraints can be added to more accurately reflectphysical realities and management objectives. Thus, value clusters areobjective, data-centric objects, such as matrices, that can be used asinputs in an optimization engine.

FIG. 21 is a block diagram illustrating process value clusters, inaccordance with an illustrative embodiment. The process of formingprocess value clusters can be implemented using a data processingsystem, such as data processing systems 104, 106, 110, 112, and 114 inFIG. 1 or data processing system 200 shown in FIG. 2. Process valueclusters can be implemented among multiple computers over a network,such as network 102 shown in FIG. 1.

To form process value clusters, process data structure models from “tobe” process model 1900 are associated with different common processsources in process value clusters. A process source can be an existingapplication, algorithm, or flow, or a similar process that is to bedeveloped. For example, process data value cluster 2100 includes processsource 2102 and process source 2104. Application process data structuremodel 1902 and Application process data structure model 1904 each takeadvantage of these process sources in process value cluster 2100.Application process data structure model 1902 also takes advantage ofprocess value cluster 2106, which contains process source 2102, processsource 2104, and process source 2108. Application process data structuremodel 1902 also takes advantage of process value cluster 2110, whichincludes process source 2102, process source 2104, process source 2108,and process source 2112. Different process data structure models areassociated with different process value clusters as shown.

FIG. 22 is a block diagram illustrating elements of a “to be” datamodel, in accordance with an illustrative embodiment. Data valueclusters shown in FIG. 22 correspond to data value clusters shown inFIG. 20.

For example, data value cluster 2000, which contains data source 2002and data source 2004, are related to a set of facts 2200. Set of facts2200 refers to a variety of references, such as reference 2202,reference 2204, reference 2206, and reference 2208. Similarly, datavalue cluster 2006, which includes data sources 2002, 2004, and 2008,are related to set of facts 2210. Set of facts 2210 refers to a varietyof references, such as reference 2212, reference 2214, reference 2216,and reference 2218. Likewise, data value cluster 2010, which includesdata sources 2002, 2004, 2008, and 2012 are related to set of facts2220. Set of facts 2220 refers to a variety of references, such asreference 2222, reference 2224, reference 2226, and reference 2228.

FIG. 22 shows that to obtain certain data certain data sources should bedeveloped or accessed and to be able to satisfy requirements of aproject. The shown boxes are connected to provide a classicrepresentation of a data model. The data value clusters shown in FIG. 22tie to those data that enable individual sub-projects to work byproducing corresponding particular output objects.

FIG. 23 is a block diagram illustrating elements of a “to be” processmodel, in accordance with an illustrative embodiment. Process valueclusters shown in FIG. 23 correspond to process value clusters shown inFIG. 21.

Process value clusters correspond to different processes in variousoptimized sub-projects. For example, process value cluster 2100, whichincludes process sources 2102 and 2104 is used by optimized sub-project1404 and optimized sub-project 1408 in FIG. 14. Similarly, process valuecluster 2106, which includes process sources 2102, 2104, and 2108, isused by optimized sub-projects 1406 and 1412. Likewise, process valuecluster 2110, which includes process sources 2102, 2104, 2108, and 2110,is used by optimized sub-projects 1400, 1404, 1406, and 1412 in FIG. 14.

The knowledge of how process value clusters relate to projects can beused to optimally select sub-projects for an optimized project. Forexample, if sub-projects 1404 and 1408 have been completed already, thenprocess value cluster 2100 is also complete. This result means thatprocess source 2102 and process source 2104 are available. If processsource 2108 were to be developed, then process value cluster 2106 wouldbe completed. Thus, sub-project 1406 and sub-project 1412 would beeasily finished. Thus, the process value clusters allow for detailed,data-centric planning of which sub-projects should be completed in whatorder. The process can be two-way: The completion of projects alsoallows process value clusters to be delivered.

FIG. 24 is an exemplary affinity matrix, in accordance with anillustrative embodiment. The process of forming an affinity matrix canbe implemented using a data processing system, such as data processingsystems 104, 106, 110, 112, and 114 in FIG. 1 or data processing system200 shown in FIG. 2. An affinity matrix can be implemented amongmultiple computers over a network, such as network 102 shown in FIG. 1.

Affinity matrix 2400 is a matrix of data that indicates a relationshipbetween groups of data sources and groups of output objects, and/orgroups of available logical processes and groups of output objects.Affinity matrix 2400 describes data sources and output objects in termsof what output objects are available based on what data sources areavailable. Thus, for example, affinity matrix 2400 can allow a user todetermine that if Output Object “X” is available because its datasources are available, then Output Object “Y” and Output Object “Z” arealso available because they use similar data sources.

Specifically, affinity matrix 2400 has a series of columns 2402reflecting existing data sources and a series of rows 2404 correspondingto data structures in FIG. 18 and FIG. 19. For columns 2402, existingdata sources include “as-is” data sources and any data sources that havebeen completed during the course of constructing the major informationtechnology project. Thus, affinity matrix 2400 evolves over time and canbe adjusted as part of a feedback process, such as feedback 708 in FIG.16. In the illustrative example shown, columns 2402 include data sourcecolumn 2406, data source column 2408, data source column 2410, datasource column 2412, and data source column 2414.

Affinity matrix 2400 also has a series of rows 2404 of data structurescorresponding to data structures in FIG. 18 and FIG. 19. For example,rows 2404 include report data structure row 1802, screen data structurerow 1804, production schedule data structure row 1806, deliverable datastructure row 1808, application data structure row 1902, applicationdata structure row 1904, flow data structure row 1906, and flow datastructure row 1908. Rows 2404 also include personal skills program datastructure row 2418 and opportunities database for sales data structurerow 2420. Rows 2418 and 2420 are newly added data structurescorresponding to newly added output objects as a result of a feedbackprocess. Thus, again, affinity matrix 2400 evolves over time and can beadjusted as part of a feedback process, such as feedback 1308 in FIG.16.

An intersection of a column and a row can be referred to as a cell. Eachcell has a number that is either zero or one. A zero indicates that adata source is not needed or is incomplete for a particular outputobject data structure in rows 2404. A one indicates that a data sourceexists and is used for a particular output object data structure in rows2404. For this reason, as the major information technology projectproceeds towards completion, more ones will appear in affinity matrix2400 until every cell has a one when the major information technologyproject is completed. Thus, for example, report data structure 1802either does not rely on or does not yet have available data sources2406, 2408, 2410, and 2412; however, report data structure 1802 usesdata source 2414. Furthermore, data source 2414 also exists and isavailable.

Affinity matrix 2400 can be used to estimate the ease or difficulty ofadding new output objects to the major information technology project.For example, personal skills program data structure 2418 has a one incolumn 2408 and opportunities database for sales data structure 2420 hasa one in column 2408 and column 2414. Given that ones already exist forthese columns in other rows, such as row 1808, one can immediatelyascertain that at least those data sources already exist and arecompleted. In fact, a one exists in at least one row for every column inaffinity matrix 2400. Thus, assuming that the personal skills programand opportunities database for sales output objects do not use someother data source not reflected in columns 2402, one can alsoimmediately ascertain that adding the personal skills program andopportunities database for sales output objects would be relativelysimple. Adding these output objects would be relatively simple becausethe data sources upon which these output rely already exist and arecompleted.

FIG. 25 is a block diagram illustrating mapping from an “as-is” model toa “to be” model, in accordance with an illustrative embodiment. FIG. 25corresponds to mapping 1612 in FIG. 16. The mapping process shown inFIG. 25 can be implemented using a data processing system, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. The mapping process shown in FIG.25 can be implemented among multiple computers over a network, such asnetwork 102 shown in FIG. 1.

“To be” model 2400 is mapped to “as-is” model 2402. During this process,“to be” data structures and “to be” process models are mapped to “as-is”data structures and “as is” processes. This mapping does not map all “tobe” data structures and “to be” processes to all desired underlying datastructures and processes, but rather to those data structures andprocesses that already exist. Thus, the mapping process shown in FIG. 25describe how “to be” data structures and “to be” processes can takeadvantage of existing, or “as is”, data structures and existing, or “asis”, processes.

In the example shown in FIG. 25, database data structure 1810, file datastructure 1812, and application data structure 1902 are all mapped tosalary database 2504, which is an existing database. In other words,each of data structures 1810, 1812, and 1902 take advantage of or usesalary database 2504. However, only application data structure 1902takes advantage of or uses current application 2506. Thus, applicationdata structure 1902 is mapped to current application 2506.

FIG. 26 is a block diagram illustrating transformation issues applied tothe mapping from an “as-is” model to a “to be” model, in accordance withan illustrative embodiment. FIG. 26 corresponds to transformation issues1018 in FIG. 16. The transformation issues shown in FIG. 26 can bedescribed as data in a data processing system, such as data processingsystems 104, 106, 110, 112, and 114 in FIG. 1 or data processing system200 shown in FIG. 2. The transformation issues described in FIG. 26 canbe implemented among multiple computers over a network, such as network102 shown in FIG. 1.

Transformation issues 2600 are issues regarding transforming “as is”data structures and processes into “to be” data structures andprocesses. Transformation issues 2600 are quantitative factors that aredefined and then provided as input to an optimization engine, such asoptimization engine 1304 in FIG. 13 and FIG. 16.

Examples of transformation issues include an estimated cost for sourceto target conversion 2602. This cost can be estimated and quantified,with the quantified value included as input in the optimization engine.Similar quantitative transformation issues include a quantitativeassessment of the difficulty for source to target conversion 2604,source data type conversion 2606, estimated risk for source to targetconversion 2608, process for mapping source to target conversion 2610and source data quality scoring 2612.

FIG. 27 is a block diagram illustrating exemplary available resources,in accordance with an illustrative embodiment. FIG. 27 corresponds toresources 1000 in FIG. 10 and in FIG. 16. Available resources 2700 inFIG. 27 can be described as data in a data processing system, such asdata processing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. Available resources 2700described in FIG. 27 can be implemented among multiple computers over anetwork, such as network 102 shown in FIG. 1. Additionally, availableresources 2700 can be considered a type of boundary conditions providedas input into an optimization engine, such as optimization engine 1304in FIG. 13 and FIG. 16.

Available resources 2700 represent the accumulation of all availableresources, as defined by the organization. Examples of availableresources include physical computer equipment 2702, physical storagecapacity 2704, training 2706, software 2708, money 2710, time available2712, physical resources 2714 (such as buildings), network capability2716, and personnel 2718. Available resources 2700 could be more,different, or fewer available resources than those shown in FIG. 27.

FIG. 28 is a block diagram illustrating exemplary project constraints,in accordance with an illustrative embodiment. FIG. 28 corresponds toconstraints 1002 in FIG. 10 and in FIG. 16. Project constraints 2800 inFIG. 28 can be described as data in a data processing system, such asdata processing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. Project constraints 2800described in FIG. 28 can be implemented among multiple computers over anetwork, such as network 102 shown in FIG. 1. Additionally, projectconstraints 2800 can be considered a type of boundary conditionsprovided as input into an optimization engine, such as optimizationengine 1304 in FIG. 13 and FIG. 16.

Project constraints 2800 represent the accumulation of all constraints,as defined by the organization. Examples of project constraints includeproblems in software, network, database, hardware mandates, andperformance characteristics 2802. Other project constraints include datastructure compatibility issues 2804, data quality issues 2806, databasecompatibility issues 2808, data quality 2810, team location and travelconstraints 2812, cash shortages 2814, organizational rigidity 2816,personal restrictions 2818, organizational policies 2820, informationtransfer policies 2822, legal constraints 2824, classified informationpolicies 2826, HIPPAA or other privacy rules 2828, hazardous informationrestrictions 2830, risk tolerance 2832, security requirements 2834,information technology (IT) policies 2836, development requirements2838, and required delivery steps 2840. Project constraints 2800 couldbe more, different, or fewer available resources than those shown inFIG. 28.

FIG. 29 is a block diagram illustrating exemplary political concerns, inaccordance with an illustrative embodiment. FIG. 29 corresponds topolitical concerns 706 in FIG. 16. Political concerns 2900 shown in FIG.29 can be described as data in a data processing system, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. Political concerns 2900 describedin FIG. 29 can be implemented among multiple computers over a network,such as network 102 shown in FIG. 1. Additionally, political concerns2900 can be considered a type of boundary conditions provided as inputinto an optimization engine, such as optimization engine 1304 in FIG. 13and FIG. 16.

Political concerns 2900 represent the accumulation of all politicalconcerns, as defined by the organization. Examples of project concernsinclude personnel management issues 2902, resource allocation issues2904, timing issues 2906, and procedural issues 2908. Each exemplarypolitical concern 2902 through 2908 is quantified as a number so that acorresponding political concern can be processed by an optimizationengine.

Examples of personnel management issues 2902 include preventing certaintypes of employees from interacting with each other. For example, alarge law firm might desire to avoid having certain employees interactwith each other in order to maintain certain privacy issues. Examples ofresource allocation issues 2904 include a desire by an organization torequire that for every dollar received by organization A, organization Bshould also receive two dollars. An example of timing issues 2906 is adesire by an organization to produce deliverables in a particular orderor within a particular time period. Examples of procedural issues 2908include a desire by an organization to require that a particularindividual within an organization receive a particular report beforesome other individual in the organization.

FIG. 30 is a block diagram illustrating examples of feedback applied toan optimization engine, in accordance with an illustrative embodiment.FIG. 30 corresponds to feedback 708 in FIG. 16. Feedback 3000 shown inFIG. 30 can be described as data in a data processing system, such asdata processing systems 104, 106, 110, 112, and 114 in FIG. 1 or dataprocessing system 200 shown in FIG. 2. Feedback 3000 described in FIG.30 can be implemented among multiple computers over a network, such asnetwork 102 shown in FIG. 1.

Feedback 3000 includes changes made to the input provided to theoptimization engine. Examples of feedback include a reasonablenessassessment 3002, feasibility assessment 3004, sensitivity analysis 3006,and change in deliverables 3008. Each exemplary type of feedback 3002through 3008 is quantified as a number so that the optimization enginecan re-perform an optimization after receiving the correspondingfeedback.

Examples of reasonableness assessment include an assessment by one ormore individuals whether a particular result is desirable. Areasonableness assessment can result in one or more adjustments to oneor more inputs to the optimization engine. Although a reasonablenessassessment involves human input, a reasonableness assessment is eitherquantified or results in a quantified change to an input in anoptimization engine. An example of a feasibility assessment 3004includes an assessment by one or more individuals that a particularresult is feasible. A feasibility assessment can result in one or moreadjustments to one or more inputs to the optimization engine. Although afeasibility assessment involves human input, a feasibility assessment iseither quantified or results in a quantified change to an input in anoptimization engine.

An example of sensitivity analysis 3006 is to adjust slightly one ormore inputs to the optimization engine and then to re-execute theoptimization process. If the final result changes dramatically as aresult of a slight adjustment, then the optimized solution, which is theoptimized major information technology project, is considered fragile.Fragile solutions are undesirable because they are subject to a highdegree of risk. Thus, one or more elements of the solution model mightbe adjusted in order to produce a stable solution that is not a fragilesolution.

An example of a change in deliverables is a change in the desired outputobjects. For example, an organization might desire to produce more,fewer, or different output objects as the planning the major informationtechnology project proceeds. Changes in these output objects change theinputs to the optimization engine.

FIG. 31 is a block diagram illustrating a computer-implemented method ofcreating optimized sub-projects for a major information technologyproject, in accordance with an illustrative embodiment. The method shownin FIG. 31 can be implemented in one or more data processing systems,such as data processing systems 104, 106, 110, 112, and 114 in FIG. 1 ordata processing system 200 shown in FIG. 2. The method shown in FIG. 31can be implemented among multiple computers over a network, such asnetwork 102 shown in FIG. 1.

FIG. 31 summarizes the counter-intuitive method of selecting a set ofoptimized sub-projects into a plan for creating an optimal projectdefinition. Instead of proceeding from a “right to left” perspectiveshown in the prior art method of FIG. 10, the illustrative embodimentshown in FIG. 31 solves the problem of planning a major informationtechnology problem from “left to right.”

In brief summary, input 3100 is fed into optimization engine 3102. Input3100 includes solution model 1302 shown in FIG. 13 and FIG. 16. Input3100 also includes boundary conditions. Boundary conditions includeresources 1000 and constraints 1002, shown in FIG. 10 and FIG. 16, aswell as political concerns, shown in FIG. 13 and FIG. 16. Input 3100also includes feedback 1308, shown in FIG. 13 and FIG. 16. Input canalso include other data, if desired.

A mathematical optimization operation is then performed on input 3100during optimization 3102. As described above, optimization operationsare known and have been implemented in available software. As a resultof the optimization operation, optimized sub-projects 3104 are selectedfor major information (IT) project 3106.

FIG. 32 is a flowchart illustrating a computer-implemented method ofcreating optimized sub-projects for a major information technologyproject, in accordance with an illustrative embodiment. The method shownin FIG. 32 can be implemented in one or more data processing systems,such as data processing systems 104, 106, 110, 112, and 114 in FIG. 1 ordata processing system 200 shown in FIG. 2. The method shown in FIG. 32can be implemented among multiple computers over a network, such asnetwork 102 shown in FIG. 1. The term “processor” as used in thedescription of FIG. 32 refers to one or more processors that arepossibly connected via a network. The definitions of various terms usedwith respect to the description of FIG. 32, and the interactions ofcorresponding objects, can be found in the description of FIG. 13through FIG. 30.

The process shown in FIG. 32 begins along two simultaneous paths. Alongthe first path, the processor receives input regarding output objects(step 3200). The processor then receives valuation data for outputobjects (step 3202).

From that point, simultaneously the processor both receives inputregarding “as-is” data sources (step 3204) and also begins decomposingoutput objects. Specifically, the processor decomposes output objectsinto data objects (step 3206) and decomposes output objects into logicalprocesses used to create the output objects (step 3210).

After decomposing output objects into data objects at step 3206, theprocessor organizes data objects into “to be” data structures.Simultaneously, the processor determines value clusters (step 3212) fromthe logical processes used to create the output objects at step 3210 andfrom the “to be” data structures at step 3208. The processor thencreates an affinity matrix (step 3214) using the information gained formthe value clusters determined in step 3212.

Returning to steps 3204 and 3208, the processor thereafter maps “to be”data structures organized in step 3208 to “as-is” data sources receivedin step 3204 (step 3216). The processor then determines processes forgetting data from the source to the target (step 3218). Step 3218 issimilar to transformation issues block 1018 in FIG. 16.

Returning to the start of the process, the processor also receives dataregarding resources (step 3220), data regarding constraints (step 3222)and data regarding political concerns (step 3224). Steps 3220, 3222, and3224 can be performed in parallel or in a different order shown in FIG.32.

Next, the affinity matrix created in step 3214, the processes forgetting data from the source to the target in step 3218, data regardingresources at step 3220, data regarding constraints at step 3222, anddata regarding political concerns at step 3224 are provided as inputinto an optimization engine. The processor, using the optimizationengine, then performs an optimization operation within the constraintsprovided (step 3226).

A determination is then made whether feedback is desired or required(step 3228). If feedback is desired or required, then the processorreceives adjustments (step 3230). The process then returns to the startof the process and the entire process is repeated, though one or moresteps of the process are modified or adjusted to take into account thefeedback. However, if feedback is not desired or required, then theprocess terminates.

The output of the optimization engine can be stored in a storage device.The output of the optimization engine is the optimized project, havingoptimally selected optimized sub-projects. A storage device can be anystorage suitable for storing data, such as but not limited to hard diskdrives, random access memory, read only memory, tape drives, floppy diskdrives, or any other data storage medium.

Thus, a computer-implemented method, computer program product, and dataprocessing system are provided for creating optimized sub-projects for aproject. Boundary conditions, input regarding output objects, and inputregarding “as-is” data sources are received. The output objects aredecomposed into data objects and the output objects are also decomposedinto logical processes used to create the output objects. Value clustersare determined. The data objects are organized into “to be” datastructures and the “to be” data structures are mapped to the “as-is”data sources. Additional processes are determined for moving data from asource to a target. An affinity matrix is created based on the valueclusters. Finally, an optimization operation is executed with anoptimization engine to produce the optimized sub-projects. Theoptimization engine takes as inputs the boundary conditions, the “as-is”data sources, the data objects, the logical processes used to create theoutput objects, the value clusters, the “to be” data structures; themapping of the “to be” data structures to the “as-is” data sources, theadditional processes for moving data from the source to the target, andthe affinity matrix.

The embodiments described herein have several advantages over knownmethods for planning various types of projects, such as majorinformation technology projects. For example, the embodiments describedherein provide data centric solution models that result indeterministically optimized projects having optimally selected optimizedsub-projects. Thus, the probability that a project planned with theembodiments described herein will succeed is much higher than projectsplanned with known methods. Additionally, projects planned according tothe embodiments described herein are very likely to result in a finalproject that operates much more efficient than a final project plannedwith known methods.

V. Advances in the Management of Chaotic Events

The previous section, Section IV, describes our prior work with regardto optimized selection of sub-projects for a major informationtechnology project. In this section, Section V, this technology and thetechnology described with respect to Section III are extended inunexpected ways to the management of chaotic events.

FIG. 33 is a block diagram of a system for chaotic event management, inaccordance with an illustrative embodiment. The system for chaotic eventmanagement shown in FIG. 33 can be referred to as system 3300. System3300 can be implemented in one or more data processing systems, such asdata processing systems 104, 106, 110, 112, and 114 in FIG. 1, or dataprocessing system 200 shown in FIG. 2. Aspects of system 3300 can alsobe implemented using the illustrative embodiments shown in FIGS. 4-6.Aspects of system 3300 can be implemented using devices and methodsshown in FIGS. 7-31.

System 3300 provides for an optimized and adaptive mechanism to generatea mathematically optimal set of decisions, sequence of decisions, andassociated information to one or more decision makers during chaoticevents. Thus, the decision makers can most effectively respond tochaotic events even when the decision makers are under considerablestress, have limited time to make decisions, are in pain, or are limitedin other ways. System 3300 generates decision sets for specific decisionmakers. System 3300 displays information to specific decision makers informats that are most appropriate for those specific decision makers.System 3300 further provides a mechanism for unifying the decisionprocess with multiple members of the decision team, and forreincorporating and dynamically processing feedback.

Thus, system 3300 provides an optimized path which will allow decisionmakers to reach mathematically optimal or near mathematically optimalsolutions that are also non-brittle. A non-brittle solution is asolution that is relatively stable when small changes are made to theparameters that are input into system 3300. In this way, system 3300creates an effective mechanism for decision makers to arrive at optimalor near optimal solutions, as mathematically defined, to complexdecision sets that arise during real world chaotic events. Examples ofthe mathematics of optimization are provided with respect to FIG. 14.

System 3300 receives a variety of inputs of data that could be useful todecision makers when managing a response to a chaotic event. Forexample, system 3300 can receive manual input 3302 and information fromnumerous databases, such as database 3304 and database 3306. System 3300can also receive input from sensors 3308 which detect various physicalparameters of a chaotic event, such as but not limited to, wind speed,explosion, presence explosion strength, rainfall, flood levels, or anyother physical measurement that may be of interest to decision makers.

System 3300 can use sensors 3308 or manual input 3302 to detect chaoticevents. In particular, chaotic event detection 3310 is used to initiatean action by decision process module 3312.

Decision process module 3312 is part of system 3300. Decision processmodule 3312 may be one or more data processing systems, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1, or dataprocessing system 200 shown in FIG. 2. Decision process module 3312 canincorporate a mathematical optimization algorithm, such as thatdescribed with respect to FIG. 14. Decision process module 3312 can takeas inputs a variety of information, such as inputs from manual input3302, databases 3304, databases 3306, sensors 3308 and chaotic eventdetection 3310.

Decision process module 3312 makes a number of determinations. Forexample, decision process module 3312 determines optimal sets ofdecisions as shown in block 3314. An optimal set of decisions is a setof a decisions arrived at by a mathematical optimization process takinginto consideration constraints, such as, for example, money, time,available skills, available resources, manual input, data from sensors,or any other data. Decision process module 3312 also determines whichdecisions need to be made in a particular order to optimally respond toa chaotic event. Thus, the term optimal set of decisions refers to a setof decisions that is mathematically determined to most efficientlydefine a solution space using algorithms, such as those presented withrespect to FIG. 14 and elsewhere herein.

Decision process module 3312 also determines an optimal decision makerset, such as in block 3316. An optimal decision maker set is a set ofindividuals and/or programs that should make decisions with respect to aresponse to the chaotic event. For example, a decision maker set caninclude a leader, such as, for example, the director of the FederalEmergency Management Agency, a number of sub-leaders, such as, forexample, sub-directors or other individuals that answer to the authorityof the director of the Federal Emergency Management Agency, a softwareprogram designed to decide when an earthquake will exceed a particularenergy threshold for a particular geographical area, another softwareprogram that predicts the future path of a hurricane, or any other setsof decision makers.

Decision process module 3312 can also determine not only the set ofdecision makers, but also the relative organization decision makers. Forexample, decision process module 3312 may recommend that the director ofthe Federal Emergency Management Agency have the authority over theentire response effort to the chaotic event. Decision process module3312 may then designate which individuals in the organization shouldhave authority to make particular decisions. These individuals may ormay not be part of the Federal Emergency Management Agency.

For example, decision process module 3312 may recognize from one or moredatabases 3304 or 3306 that a particular professor of geology hasparticular expertise with respect to an earthquake that occurred in aparticular geographical area. Decision process module 3312 can thenrecommend that the particular professor have secondary decision makingpower with respect to particular aspects of the response to theearthquake and that the particular professor should only answer to thedirector of the Federal Emergency Management Agency.

Decision process module 3312 also determines an optimal decision order,as shown in block 3318. Decision process module 3312 specificallydetermines the order in which decisions should be made. Additionally,decision process module 3312 also determines subsets of decisions withinthe decision order. Thus, for example, decision process module 3312 cancreate sets and subsets of decisions to be made in a particular order byparticular decision makers within a hierarchy of decision makers.

Decision process module 3312 also determines an optimal display ofinformation as shown in block 3320. An optimal display of information isa display of information that, mathematically speaking, displaysinformation in a most efficient format with respect to any givendecision maker. For example, the professor of geology mentioned abovemay receive complex data in the form of matrices when informationregarding the earthquake is displayed to the professor. However, in thisparticular example, the director of the Federal Emergency ManagementAgency does not have special technical expertise with respect toearthquakes in the particular geographical area. Therefore, decisionprocess module 3312 will cause information to be displayed at a lesstechnical level to the director of the Federal Emergency ManagementAgency. In block 3320, a display of information can also be adjustedaccording to the communication bandwidth that is available and accordingto user input, user desires, user skill level, or many other differentparameters.

Decision process module 3312 operates using a massively recursiveprocess. Thus, decision process module 3312 continually updates each ofthe determinations made in blocks 3314, 3316, 3318 and 3320.Additionally, decision process module 3312 takes as additional inputduring each iteration any new information that may arrive, decisionsthat are made at any particular point, as well as the output of previousiterations of decision process module 3312.

Thus, for example, the output of decision process module 3312, withrespect to a previous iteration of determining optimal sets of decisionsin block 3314, will become part of the input in a current and/orsubsequent iteration of the mathematical optimization process ofdecision process module 3312. Similarly, the output of optimal decisionmaker sets, optimal decision order, and optimal display of informationis also fed back into decision process module 3312 for additionaliterations of the mathematical optimization algorithm. Thus, blocks3314, 3316, 3318 and 3320 are all shown as interacting with each othervia the arrows shown in FIG. 33.

Thus, system 3300 is capable in times of chaos of initiating a decisionprocess based on manual input, sensor input or any other input, andproducing inferential operations on information stored in databases inorder to determine optimal decision sets, optimal decision maker sets,optimal decision order, and optimal display of information. These setsare subject to review, feedback, and modification in future iterationsof a mathematical optimization algorithm. The processes in decisionprocess module 3312 are adaptive based on cause, scope, and results ofthe chaotic situation. Decision process module 3312 determinesavailability of decision makers and adapts accordingly. Thus, forexample, if decision makers become unavailable due to losses that occurduring the chaotic event, or if additional decision makers becomeavailable during the response to the chaotic event, decision processmodule 3312 updates the sets of decisions, the optimal decision makerset, the order of decisions, and the display of information for eachparticular decision maker accordingly.

Decision process module 3312 also further determines which decisionmaker should make decisions and in what particular order the decisionsshould be made. Decision process module 3312 subdivides the generateddecision sets into manageable units of work chunks. In this manner,decision process module 3312 determines an optimal order of questions tobe asked and decisions to be made in order to minimize cognitiveoverhead of decision makers and to maximize an efficiency of thedecision making process when responding to a chaotic event.Additionally, decision process module 3312 determines an adaptive andpersonalized set of displays to maximize the efficiency of eachindividual decision maker.

Decision process module 3312 and system 3300 are also sensitive toavailable resources, such as communication bandwidth and display types.The net effect of the combined systems and methods is to optimize anentire decision cycle in times of chaos, accounting for dynamicallychanging conditions, different sets of decision makers, different typesof decision styles, political considerations encoded as mathematicalconstructs, and capabilities of decision makers, with the goal ofenabling the decision makers to reach a non-brittle, mathematicallyoptimal or near optimal solution to the complex decision sets that arisewhen responding to real world chaotic events. As used herein, the termnon-brittle means that a particular solution does not changedramatically in response to a small change in the input to themathematical optimization algorithm.

Decision process module 3312 can also use mathematical heuristics toeliminate decisions from the set of variable decisions that would beconsidered to be undesirable or otherwise a waste of time. For example,an initial set of decisions can be generated, wherein one of thedecisions is whether to send helicopter rescue operations into an areabeing battered by a high-end force five hurricane. In this particularcase, making such a decision would be considered an undesirable waste oftime because the answer would be considered to be obviously a ‘no’.Thus, heuristic mathematical techniques can be used to eliminate suchdecisions from the decision making process.

However, decision process module 3312 can incorporate user input withrespect to decisions that the user desires to make. Thus, for example,even if the heuristics of decision process module 3312 were to eliminatethe decision regarding helicopter rescue missions, a particular humandecision maker could potentially initiate such a decision regardless ofthe fact that decision process module 3312 did not present that decisionto the decision maker.

System 3300 and decision process module 3312 are intelligent systems inthat system 3300 and decision process module 3312 can learn byincorporating solution outputs as inputs. Additionally, system 3300 anddecision process module 3312 learn in response to continuing manualinput and input from various data sources. For example, as decisionmakers practice for response to a chaotic event, decision process module3312 and system 3300 incorporate all of the input and decisions made bydecision makers back into the mathematical optimization algorithm. As aresult, at each iteration, system 3300 and decision process module 3312produce results that are more likely to correspond to expectations ofthe decision makers. Additionally, as system 3300 and decision processmodule 3312 receive continuing input and decisions regarding a responseto an actual chaotic event, decision process module 3312 and system 3300further update the list of decision makers, sets of decisions to bemade, the order in which decisions are to be made, and the display ofthe decisions and other information. Thus, system 3300 and decisionprocess module 3312 constantly update and refine these elements in orderto maximize the efficiency of a response to a chaotic event,particularly with regard to large, complex responses to large scalechaotic events.

FIG. 34 is a block diagram of an additional function for a system forchaotic event management, in accordance with an illustrative embodiment.The system for chaotic event management shown in FIG. 34 is the same assystem 3300 shown in FIG. 33. Thus, the system for chaotic eventmanagement can be referred to as system 3400.

System 3400 includes display module 3402. Display module 3402 is used todetermine how to display decisions and information to any givenparticular decision maker. Display module 3402 takes as input a varietyof information, including but not limited to, user profile 3404, userlimitations 3406, decision maker level 3408, changing conditions 3410,learning 3412, communications capability 3414, decision maker expertise3416, user input 3418, available resources 3420, and transportationcapacities 3422.

User profile 3404 can include a variety of information, such as but notlimited to, the name of the user, the skills of the user, a user input,an education of the user, a rank of the user within a particularorganization, a limitation of the user, a cultural fact regarding theuser, a subject area of interest of the user, a priority of the user, ahierarchy of users, and other information that might be relevant todescribe a particular user. User profile 3404 can also be used withregard to decision process module 3312 of FIG. 33 for determiningoptimal sets of decisions, optimal decision maker sets, optimal decisionordering, and other aspects of the presentation of decisions to be madein response to a chaotic event.

User limitations 3406 may be a part of user profile 3404 or may bestored as data sets apart from user profile 3404. User limitations 3406include, for example, a fact that a user is blind, a fact that a user isdeaf, a fact that the user does not have a mathematical background whenthe user is responsible for a decision that requires mathematicalknowledge, a fact that a user is unavailable until a particular time, afact that a user is injured, or any other particular limitations thatmight apply to a particular user. These limitations are used indetermining how to display a set of decisions or other information tothat user. For example, if a user is deaf, then all information isprovided in picture or text format. Similarly, user limitations 3406 canbe used when determining whether or not a particular user should be thedecision maker at a particular level.

Similarly, decision maker level 3408 can also influence how displaymodule 3402 displays information to a particular user. The term“decision maker level” is synonymous with the term “decision makerrank.” For example, when decision maker level 3408 is above a certainpoint, then display module 3402 may display not only decisions sets thatare pertinent to that particular user, but also decision sets pertinentto other users in order that the high level decision maker can directlyor manually influence the decision sets of lower level individuals inthe organization. In contrast, a decision maker level 3408 can be usedto limit the availability of information to low level decision makers. Adecision maker level 3408 can also be used to adjust what type ofinformation is displayed to a particular user.

Changing conditions 3410 also influence how display module 3402 displaysinformation and how decision process module 3312 of FIG. 33 performs itsfunctions. For example, if a condition of a chaotic event changes, thendisplay module 3402 may display information relevant to the change inorder to call attention of the change to the decision maker. Forexample, if a new tornado warning is issued, then display module 3402can be used to adjust the display for a particular decision maker toindicate that a tornado has been spotted in the decision maker's area.Changing conditions 3410 can also be used to change the sets ofdecisions to be made as determined in decision process module 3312 ofFIG. 33.

Additionally, display module 3402 can receive as input learning 3412. Asdescribed above, display module 3402 receives input and feedback.Display module 3402 uses that information to determine an optimal methodof displaying information to a particular user, as well as to determinehow that information is to be displayed.

Communications capability 3414 also can be used as input into displaymodule 3402 to determine how to display information to a particularuser, or what information to display to a particular user. For example,if a specialized surgeon is assisting a general surgeon to perform aprocedure over a long distance, then display module 3402 takes intoaccount the communications capability 3414 between the specialistsurgeon and the general surgeon. For example, if communicationscapability 3414 is a high speed connection, then display module 3402 maycause available pictures and/or video of the ongoing surgical procedureto be communicated to the display of the specialist surgeon. However, ifcommunications capability 3414 is not sufficient to transfer such videoor picture information, then display module 3402 may cause audioinformation or text information to be displayed to the specialistsurgeon. Similarly, the communications capability 3414 available to thegeneral surgeon is also taken into account in determining how displaymodule 3402 displays information to the general surgeon.

Decision maker expertise 3416 is also used as input into display module3402 to determine how to display information and what information todisplay to a particular user. For example, if a decision maker isconsidered an expert with respect to a particular aspect of the chaoticevent, then display module 3402 may cause complex, technically detailedinformation to be displayed or otherwise transmitted to the particulardecision maker. In contrast, if the decision maker does not haveparticular expertise, then display module 3402 may cause differentinformation or less technically oriented information to be displayed tothe particular decision maker.

User input 3418 can also be used as input into display module 3402 inorder to determine how information is displayed and what information todisplay to the particular user. For example, if a particular decisionmaker does not have expertise in a particular aspect of responding tothe chaotic event, but that particular user desires and has sufficientrank to obtain detailed technical data, user input 3418 can be used tocause display module 3402 to retrieve and display such information tothe particular user. Additionally, user input 3418 can be used todetermine how information is displayed, such as, for example, an audioformat, video format, text format, outline format, trees format, or anyother particular format of interest to the particular user.

User input 3418 can also be used to secure certain information frombeing displayed to other users of lower rank. This function isespecially used in the case where classified data is used in determiningoptimal decision sets, optimal decision order, optimal decision makers,and optimal display types. Note that secure information, such asclassified or secret information, can also possibly be used by decisionprocess module 3312, but not displayed to those not authorized to seesuch information. A flag or tag can be used to mark information orinformation sources as being classified, secret, or otherwise secured.For example, in the case of a major terrorist attack on a nuclearweapons facility, certain secret information regarding the facility canbe used in determining what decisions need to be made, but the decisionprocess module restricts such information to decision makers having theauthority to see the information. In extreme cases, the identities ofdecision makers can be kept secret from each other.

Additionally, available resources 3420 can be used as input into displaymodule 3402 in order to determine how information should be displayed orwhat information to display to a particular user. For example, if system3400 determines that a particular decision maker does not have anyavailable resources 3420 at a particular time, display module 3402 maycause the particular display of that particular user to show that noaction is to be taken due to lack of available resources 3420. However,when such resources become available, then display module 3402 willcause the display of that particular user to be updated to reflectdecisions that can be made with respect to responding to the chaoticevent.

Additionally, transportation capacities 3422 can be used as input intodisplay module 3402 in order to determine how information should bedisplayed or what information to display to a particular user. Forexample, if enough trucks and airplanes are available to move neededsupplies to a disaster area, then the logistics portion of the decisionmaking process can be simplified so that, for example, a decision makerneed not decide what supplies should be sent. However, if transportationcapacity is sub-optimal, then display module 3402 can alter the displayto also include a list of available supplies and a decision tree as towhich supplies should be sent. Likewise, display module 3402 can alterthe display to include types of transportation available and a decisiontree as to how supplies should be sent.

Taken together, display module 3402 in conjunction with system 3400 cancreate many different types of displays. For example, display module3402 can create management display 3422, which is adapted to mostefficiently assist a manager or high level decision maker during thecourse of his or her duties. Additionally, display module 3402 cancreate expert display 3424. Expert display 3424 is particularly orientedtowards use by an expert, and thus is more likely to be oriented toconvey technical information or technical data. Display module 3402 canalso be used to create limited bandwidth display 3426. Limited bandwidthdisplay 3426 displays information in a form that can be transmitted overthe available bandwidth. For example, if the bandwidth is large enoughfor audio communications but too small for video communications, thenaudio and text information may be communicated to a particular display.

Display module 3402 can also be used to create technician display 3428.Technician display 3428 can be oriented towards assisting decisions thata particular technician operating on a particular aspect of a problemcaused by the chaotic event is to make. For example, if a hurricaneknocks out a power transformer, then display module 3402 can display anelectrical circuit diagram of the transformer to the technician.Additionally, display module 3402 can create a change condition display3430. Change condition display 3430 alerts a user to changed conditionsand may include multiple forms of display, such as, for example,flashing video, color, audio alarms, or other means for displayingchanged conditions.

FIG. 35 is an exemplary screenshot of an output of a system for chaoticevent management, in accordance with an illustrative embodiment. Theexemplary screen shot shown in FIG. 35 can be created using a displaymodule and a system for chaotic event management, such as display module3402 and system 3400 of FIG. 34. In particular, screen shot 3500 can berendered using a data processing system, such as data processing systems104, 106, 110, 112, and 114 in FIG. 1, or data processing system 200shown in FIG. 2.

Screen shot 3500 shows an exemplary management display, such asmanagement display 3422 in FIG. 34. Thus, for example, window 3502 showsa list of all available decision makers at the various levels. Window3502 also shows the hierarchy of decision makers. The system for chaoticevent management can receive input from the user with respect todecision makers in order to change the hierarchy shown in window 3502.

Additionally, window 3504 shows a set of decisions and an order in whichthose decisions should be made. For example, window 3504 shows that thefirst decision that the decision maker should make is to assignmanagement teams. Window 3504 also shows a subset of decisions withinthe first decision, such as to designate team leaders, deploy grossresources, and designate team leader access.

In this particular example, the chaotic event is a levee breach. Thus,the system for chaotic event management recommends, after assigningmanagement teams, that the decision maker decide whether to deploysearch and rescue helicopters, then to decide whether to deploy RedCross resources, and finally, to decide whether to deploy leveeengineers.

FIG. 36 is an exemplary screenshot of an output of a system for chaoticevent management, in accordance with an illustrative embodiment. Screenshot 3600 is an exemplary screen shot that can be created using displaymodule 3402 of system 3400 shown in FIG. 34. Screen shot 3600 can berendered by data processing systems, such as data processing systems104, 106, 110, 112, and 114 in FIG. 1, or data processing system 200shown in FIG. 2.

In particular, screen shot 3600 shows a subset of decisions to be madeby an expert at the scene of a particular levee breach. Screen shot 3600is different than screen shot 3500 shown in FIG. 35 because the role ofthe expert is different than that of the manager. For example, theexpert has different concerns and makes different decisions compared tothe leader of the response effort.

Thus, for example, screen shot 3600 shows window 3602, which includes aset of decisions and an order in which the decisions should be made. Inthis example, the expert should first assess if a determinationphysically and securely can be made to determine if a new breach hasoccurred. In the second decision, the expert should assess the currentlevee breach. In the third decision, the expert should assess thelikelihood of a first technique to succeed in damming the breach.

To assist the expert user, screen shot 3600 includes window 3604, whichincludes a variety of data that the expert can access. For example, theexpert could access breach locations, levee data, such as, for example,dimensions or type of levee, erosion statistics, water flow at aparticular rate over a particular type of material, three-dimensionalprofiles of the main breach, a topological map of the geographical areain which the breach took place, and a list of available resources forresponding to the breach.

Screen shot 3600 also shows window 3608, which shows the current tasksthat the expert is involved in, such as, for example, to repair leveebreaches. Screen shot 3600 also includes window 3606, which shows theposition of the decision maker in the hierarchy of decision makers. Thisinformation may be useful in case the expert needs to consult a higherlevel decision maker when making a particular decision, or to requestadditional resources.

Additionally, window 3606 can be used to show contact information forthe other users in the decision hierarchy. Additionally, window 3606 canbe used to show other individuals that may have skills that the expertdetermines would be of use in performing the task of inspecting andrepairing the levee breach.

FIG. 37 is an exemplary screenshot of an output of a system for chaoticevent management, in accordance with an illustrative embodiment. Display3700 is a display that can be created using display module 3402 usinginformation from system 3400 in FIG. 34. Screen shot 3700 can berendered using a data processing system, such as data processing systems104, 106, 110, 112, and 114 in FIG. 1, or data processing system 200shown in FIG. 2.

Screen shot 3700 is relatively simple, showing a small amount of text.In constructing screen shot 3700, display module 3402 in FIG. 34receives as input that screen shot 3700 would have to be displayed on atiny display window of an onboard system of a helicopter having no audiocapability and limited bandwidth capability. Thus, display module 3402of FIG. 34 sends a very simple text message to the display on screenshot 3700.

Additionally, display module 3402 and system 3400 of FIG. 34 recognizefrom available input that the helicopter pilot is flying in an intensestorm and will have limited ability to assess complex instructions or toassess complex decisions because the pilot will be distracted by theneed to safely pilot the aircraft. As a result, display module 3402 ofFIG. 34 causes the simple instruction shown in screen shot 3700 to bedisplayed. In particular, screen shot 3700 shows the text message “ifyou can fly, then search over northeast part of city, 1 mile north ofI-777.” This simple message can be quickly and easily understood by thehelicopter pilot, who can then make an appropriate decision as towhether or not the helicopter pilot can proceed to the area where arescue is needed.

FIG. 38 is a flowchart illustrating an operation of a system for chaoticevent management, in accordance with an illustrative embodiment. Theprocess shown in FIG. 38 can be implemented using a system for chaoticevent management, such as system 3300 shown in FIG. 33 or system 3400shown in FIG. 34 and can be further implemented using one or more dataprocessing system, such as such as data processing systems 104, 106,110, 112, and 114 in FIG. 1, or data processing system 200 shown in FIG.2.

The process begins as the system receives notification of a chaoticevent (step 3800). The system then performs a decision process (step3802). The decision process is implemented using mathematical heuristicalgorithms and a mathematical optimization algorithm based upon inputfrom a variety of sources. Sources can include input on constraints,available resources, data regarding a chaotic event, manual input fromsensors measuring a chaotic event, and possibly many other types ofinputs.

The system then simultaneously performs four determinations. The systemdetermines an optimal decision maker set in step 3804. An optimaldecision maker set is a set of decision makers that should makedecisions with respect to responding to the chaotic event. The systemalso determines an optimal set of decisions (step 3806). An optimal setof decisions is a set of decisions that is mathematically determined,based on available inputs, to most efficiently respond to a particularchaotic event. An optimal set of decisions represents a mathematicalsolution to an optimization process using an objective function, modelformulation, constraints and available resources.

The process also determines an optimum decision order (step 3808). Anoptimal decision order is a mathematically determined order in whichdecisions should be made in order to maximize the efficiency of responseto the chaotic event. Again, the optimum decision order is an output ofa mathematical optimization algorithm. The system also determines anoptimal display of information (step 3810). An optimal display ofinformation is a display of information mathematically determined tomaximize efficiency of display of information to one or more decisionmakers. An example of optimally determined display information can befound in FIG. 35 through FIG. 37.

The system receives each of the determinations in steps 3804 through3810 (step 3812). The system then receives manual input and/or automaticinput (step 3814). During this step, the system receives input fromusers, decision makers, sensors, or one or more data sources that mightbe required or desired. The input can also include a decision template.A decision template is a data structure that defines a structure of aset of decisions for a particular type of chaotic event. A decisiontemplate can include sample decisions that are to be made with respectto a particular type of chaotic event.

The system then determines whether to iterate the decision processbefore displaying information (step 3816). The determination of whetherto iterate the decision process is made primarily by first iterating thedecision process, changing the iteration process slightly anddetermining if a slight change leads to a greatly different result. Aresult that is greatly different based on a small change in initialconditions is referred to as a brittle answer. In most cases, the systemwill reprocess the model over a range of inputs to determine changes tothe optimal solution. If the optimal solution is brittle, the systemuser can be presented additional information about the range of newanswers, given changes to the input values. If the slight change resultsin little substantial change in the final result, then the processiteration is complete.

Therefore, at step 3816, if the system decides that iteration of thedecision process is to be performed, then the process returns to step3802 and repeats. Otherwise, if the decision process is not to beiterated, the system displays the decision maker set (step 3818). Thesystem then displays the decision set and decision order to respectivedecision makers according to an optimal display method (step 3820).

The system then performs multiple functions simultaneously. The systemreceives decisions from decision makers as those decisions are made(step 3822). The system also receives feedback (step 3824). Feedbackincludes user input, results of prior decision processes, such asprevious optimal decision maker sets, previous optimal sets ofdecisions, previous optimal decision orders, and previous optimaldisplays. Feedback also includes input regarding the chaotic event, suchas changing conditions of the chaotic event.

The system also receives other new information (step 3826). Other newinformation can include changes to information related to the chaoticevent, such as, for example, the fact a tornado was generated within ahurricane, the fact that a levee breach was caused by a hurricane, ormeasurements of changes of wind speed of the hurricane, or otherinformation. Other new information can include additions to orsubtractions from the set of available decision makers, new availabledata, loss of available data, loss of monitoring devices, newcommunications ability, loss of communications ability, new resources,loss of resources, or any other new information that could be relevantto the decision making process.

The system then determines whether a new iteration is desired orrecommended (step 3828). A new iteration is desired or recommended inmost cases as soon as any decision is received in step 3822, feedback isreceived in step 3824, or other new information is received at step3826. However, in other embodiments, a new iteration may not be desiredor recommended when processing power is limited as a result of thechaotic event. In this case, a delay between iterations may berecommended in order to most efficiently use available processing power.If a new iteration is desired or recommended, then the process returnsto step 3802 and repeats. If a new iteration is not desired orrecommended, then the system determines whether to end the process (step3830). If the process is not to end, then simultaneously the systemcontinues to perform steps 3822, 3824, and 3826. Otherwise, the processterminates.

FIG. 39 is a flowchart illustrating a process of sub-dividing a decisionset, in accordance with an illustrative embodiment. The process shown inFIG. 39 can be implemented using a system for chaotic event management,such as system 3300 shown in FIG. 33 or system 3400 shown in FIG. 34.The process shown in FIG. 39 can be implemented in one or more dataprocessing systems, such as data processing systems 104, 106, 110, 112,and 114 in FIG. 1, or data processing system 200 shown in FIG. 2.

The process begins as the system receives information on decision makers(step 3900). Information on decision makers can include user profilesand other information regarding decision makers. A user profile caninclude a variety of information, such as skill of the user, user input,education of the user, a limitation of the user, a cultural factregarding the user, political influence of the user, a rank of the userwithin an organization, a name of the user, a subject area of interestof the user, a priority of the user, and a hierarchy of users, althoughother information can be included in a user profile.

The process then retrieves stored templates for decision types anddecision maker types (step 3902). A template for a decision type is adata structure which contains a set of difficult decisions for aresponse to a particular chaotic event type or is a data structure thatis adapted to receive information or output from a chaotic eventmanagement system, such as system 3300 shown in FIG. 33 or system 3400shown in FIG. 34. A decision maker type template is a data structurethat holds information relevant to different types of decision makers,such as, but not limited to, managers, experts, technicians, policepersonnel, fire personnel, government officials, Federal EmergencyManagement Agency officials, National Guard officers, NationalTransportation Safety Board investigators, military personnel, or otherprofessionals. A decision maker type template can also be a datastructure for holding an output of a system for chaotic eventmanagement, such as system 3300 shown in FIG. 33 or system 3400 shown inFIG. 34.

The system then incorporates prior learning (step 3904). Prior learningincludes any feedback incorporated into the system and includes prioroutputs of the system. The system then performs a heuristic selectionprocess to eliminate unsatisfactory results (step 3906). A heuristicselection process is defined as a mathematical heuristic selectionmethod. Unsatisfactory results are results that are deemed to beunsatisfactory to a user or decision maker.

The system then performs a mathematical optimization algorithm tosubdivide the resulting decision set (step 3908). By subdividing thedecision set, the system creates small work chunks that are more easilymanaged by the decision maker. Thus, the decision maker is not facedwith an overwhelming set of decisions or a set of decisions that areconsidered to be too difficult to make under stress or in a limitedamount of time. The process of performing optimization to subdivide adecision set can be implemented using the methods and devices describedwith respect to FIG. 13 through FIG. 31.

The system then receives any additional feedback (step 3910). Feedbackcan include the output of the heuristic selection process, the output ofprevious mathematical optimization algorithms with respect to creatingthe initial decision set, subdividing a decision set, or the selectionof decision makers. Feedback can also include user input, a change infacts regarding the chaotic event, or other types of new information.

The system then determines whether to perform recursion (step 3912). Arecursion process should be performed any time a feedback is received.However, in some cases where processing power is limited, the recursionprocess may be performed only within certain time intervals or only whencertain important facts, as determined by a user, are received. Ifrecursion is to be performed, then the process returns to step 3908 andrepeats. Otherwise, if no recursion is to be performed at this point orif continual recursion is performed, then the system stores optimallyselected decision sets (step 3914).

The system then determines whether to iterate the entire process (step3916). Ideally, the process should be iterated continuously as newfeedback is continuously received. However, iteration may be limited toa particular number of times or to receipt of particular types ofinformation or receipt of what is determined to be important informationin order to conserve processing power. If the process is to iterate,then the process returns to step 3900 and repeats. Otherwise, theprocess terminates.

FIG. 40 is a flowchart of a process of sequencing a set of decisions, inaccordance with an illustrative embodiment. The process shown in FIG. 40can be implemented in a system for chaotic event management, such assystem 3300 shown in FIG. 33 or system 3400 shown in FIG. 34. Theprocess shown in FIG. 40 can be implemented in a data processing system,such as data processing systems 104, 106, 110, 112, and 114 in FIG. 1,or data processing system 200 shown in FIG. 2.

The process begins as the system receives an optimally selected decisionset (step 4000). The system then optimizes a subset of decisions withrespect to decision order (step 4002). The subset of decisions is withinthe optimally selected decision set. The system then determines whetheradditional subsets of decisions should be ordered (step 4004). Ifadditional subsets of decisions are to be ordered or otherwisesequenced, then the process returns to step 4002 and repeats.

If no additional subsets of decisions are to be ordered or sequenced atstep 4004, then the system generates an ordered decision set withfocused supporting data (step 4006). An ordered decision set is a set ofdecisions to be taken in a particular order. Focused supporting data isdata that supports the selected order of decisions. Focused supportingdata is also data that supports the reason why the decisions wereselected or the order of decisions was designated. Focused supportingdata is also data to be used by the decision maker to make a particulardecision.

The system then displays the ordered decision set for a particulardecision maker (step 4008). Thus, multiple decision makers can receivedifferent sets of decisions with different sequences. Which users ordecision makers receive which sets of decisions in any given particularorder depends on a user profile of each particular user.

The system then determines whether interaction with decisions, otherdecision makers, and other information is to be performed (step 4010).If such an interaction is to be performed, then the system generates anew optimally selected decision set (step 4012). The process thenreturns to step 4000 and repeats. However, if an interaction is not tobe performed, then the process terminates.

FIG. 41 is a flowchart illustrating a process of generating optimaldecision sets, in accordance with an illustrative embodiment. Theprocess shown in FIG. 41 can be implemented using a system for chaoticevent management, such as system 3300 shown in FIG. 33 or system 3400shown in FIG. 34. The process can also be implemented using one or moredata processing systems, such as data processing systems 104, 106, 110,112, and 114 in FIG. 1, or data processing system 200 shown in FIG. 2.

The process begins as the system uses a mathematical optimizationalgorithm to select an optimal decision set for a user, wherein themathematical optimization algorithm takes as input a decision template,chaotic event information regarding a chaotic event, and a user profile(step 4100). The system then displays the optimal decision set (step4102). The system then determines whether any change in input occurs(step 4104). A change in input can occur as a result of previous outputsof the system, new inputs from the user, new inputs of information fromdata sources, or changes in information regarding a chaotic event.

If a change in input occurs, then the system reiterates the mathematicaloptimization algorithm to select a second optimal decision set, whereinthe mathematical optimization engine takes as further input the change(step 4106). The system then displays the second optimal decision step(step 4108). The system then determines whether to continue the process(step 4110). If the process is to continue, then the process returns tostep 4104 and repeats. Note that a ‘no’ result to the change in inputdecision at step 4104 results in the process skipping to step 4110. Ifthe process at step 4110 does not continue, then the process terminates.

FIG. 42 is a flowchart illustrating a process of generating a set ofdecisions, in accordance with an illustrative embodiment. The processshown in FIG. 42 can be implemented in a system for chaotic eventmanagement, such as system 3300 shown in FIG. 33 or system 3400 shown inFIG. 34. The system shown in FIG. 42 can also be implemented using oneor more data processing systems, such as data processing systems 104,106, 110, 112, and 114 in FIG. 1, or data processing system 200 shown inFIG. 2.

The process begins as the system receives a plurality of decisionsrelated to a chaotic event (step 4200). The system then uses a heuristicalgorithm to eliminate a first subset of decisions, wherein the firstsubset of decisions is in the first plurality of decisions, wherein asecond plurality of decisions is formed, and wherein the secondplurality of decisions comprises the first plurality of decisions lessthe first subset of decisions (step 4202).

The system then uses a mathematical optimization algorithm to select asecond subset of decisions, wherein the second subset of decisions arewithin the second plurality of decisions, and wherein the mathematicaloptimization algorithm takes as input at least one constraint andchaotic event information (step 4204). The system then stores the secondsubset of decisions (step 4206).

The system then uses the mathematical optimization algorithm to furthersubdivide the second subset of decisions into a plurality of subsets ofdecisions (step 4208). The system assigns corresponding ones of theplurality of subsets of decisions to corresponding decision makers of aplurality of decision makers, wherein assigning is based oncorresponding user profiles of the plurality of decision makers (step4210).

The system then displays the corresponding ones of the plurality ofsubsets of decisions on corresponding displays of the correspondingdecision makers (step 4212). The process terminates thereafter.

In an illustrative embodiment, the at least one constraint in theprocess shown in FIG. 42 can be many different types of constraints.Exemplary constraints include a user profile, a priority, a list ofpriorities, a stored decision template, a previously determined subsetof decisions, a mathematical characterization of a politicalconsideration, available resources, a communication method, a timeallowed to perform a task, risk tolerance, data quality, datareliability, a physical measurement or calculation related to thechaotic event, a monetary limitation, a classified information policy, asecurity requirement, and a hazardous material restriction. Numerousother types of constraints can be included when executing the process ofFIG. 42.

In another illustrative embodiment, the second subset of decisions canbe displayed. Additionally, a display window on the display can bealtered based on the user profile. Thus, depending on the type ofdecision maker, upon user preferences, or upon other information, thetype of information displayed and how the information is displayed canbe altered with respect to each individual decision maker.

In an illustrative embodiment, the first subset of decisions can be anunsatisfactory decision as determined by at least one user. Anunsatisfactory decision can be, for example, a decision that would beimmediately obvious, a decision that does not make sense within thecontext of the chaotic event, or other decisions that may be consideredto be unsatisfactory.

In another illustrative embodiment, the process can be extended byreceiving a fact in the form of at least one datum. A fact is includedin the at least one constraint to form a modified set of constraints.The mathematical optimization algorithm can be reiterated to select athird subset of decisions, when the third subset of decisions are withinthe second plurality of decisions, wherein the third subset of decisionsis different than the second subset of decisions, and wherein themathematical optimization algorithm takes as further input the modifiedsubset of constraints. The resulting third subset of decisions is thenstored.

The third set of decisions can then be further subdivided as describedabove with respect to FIG. 42. The fact in question can be any number offacts including, but not limited to, a change in the chaotic eventinformation, a removal of a constraint, an addition of a constraint, anaddition of a new decision maker, a removal of a decision maker, achange in rank of a decision maker, a decision rendered by a decisionmaker, user input and combinations thereof.

FIG. 43 is a flowchart illustrating a process of optimizing a sequenceof decisions, in accordance with an illustrative embodiment. The processshown in FIG. 43 can be implemented in a system for chaotic eventmanagement, such as system 3300 shown in FIG. 33 or system 3400 shown inFIG. 34. The process shown in FIG. 43 can be implemented using a dataprocessing system, such as data processing systems 104, 106, 110, 112,and 114 in FIG. 1, or data processing system 200 shown in FIG. 2.

The process begins as the system receives a plurality of decisionsrelated to a chaotic event (step 4300). The system then uses amathematical optimization algorithm to select a sequence in which theplurality of decisions are to be considered, wherein the mathematicaloptimization algorithm takes as input at least one constraint andchaotic event information (step 4302). The system then stores thesequence (step 4304).

The system then receives a fact in the form of at least one datum (step4306). The system includes the fact in the at least one constraint toform a modified set of constraints (step 4308).

The system reiterates the mathematical optimization algorithm to selecta second sequence of decisions, wherein the second sequence of decisionsis different from the first sequence of decisions, and wherein themathematical optimization algorithm takes as further input the modifiedsubset of constraints (step 4310). The system then stores the secondsequence of decisions (step 4312). The process terminates thereafter.

In an illustrative embodiment, the sequence of decisions ismathematically optimized to achieve a particular goal in the shortestpossible time. For example, a goal may be to contain a levee breach, ora goal may be to rescue individuals within a particular geographicallocation. Thus, in an illustrative embodiment, the sequence of decisionsis mathematically optimized to bound a solution space in the shortestpossible time.

FIG. 44 is a flowchart illustrating a process of generating an optimalsequence of decisions, in accordance with an illustrative embodiment.The process shown in FIG. 44 can be implemented in a system for chaoticevent management, such as system 3300 shown in FIG. 33 or system 3400shown in FIG. 34. The process shown in FIG. 44 can be implemented usingone or more data processing systems, such as data processing systems104, 106, 110, 112, and 114 in FIG. 1, or data processing system 200shown in FIG. 2.

The process begins as the system receives a plurality of decisionsrelated to a chaotic event (step 4400). The system then uses amathematical optimization algorithm to select a sequence in which theplurality of decisions are to be considered, wherein the mathematicaloptimization algorithm takes as input at least one constraint andchaotic event information (step 4402).

The system then stores the sequence (step 4404). The system thenreceives a fact in the form of at least one datum (step 4406). Thesystem includes the fact in the at least one constraint to form amodified set of constraints (step 4408).

The system then reiterates the mathematical optimization algorithm toselect a second sequence of decisions, wherein the second sequence ofdecisions is different from the first sequence of decisions, and whereinthe mathematical optimization algorithm takes as further input themodified subset of constraints (step 4410). The system then stores thesecond sequence of decisions (step 4412). The process terminatesthereafter.

In an illustrative embodiment, resource information regarding resourcesuseful for responding to the chaotic event is received in the system.The mathematical optimization algorithm takes as further input theresource information.

In another illustrative embodiment, the system monitors for change inthe chaotic event information. Then, responsive to detecting the change,the system reiterates the mathematical optimization algorithm to selecta second optimal decision set, wherein the mathematical optimizationalgorithm takes as further input the change. The system then displaysthe second optimal decision set for the user.

In another illustrative embodiment, the system receives information thata second user is a decision maker with respect to a chaotic event,wherein the second user has a second user profile. The system reiteratesthe mathematical optimization algorithm to select a second optimaldecision set for the user, and a third optimal decision set for thesecond user, when the mathematical optimization algorithm takes asfurther input the second user profile.

The system then displays the second optimal decision set on a firstdisplay associated with the user, and wherein the second optimaldecision set is further displayed on a first window of the firstdisplay. The system also displays this third optimal decision set on asecond display associated with the second user, wherein the thirdoptimal decision set is further displayed on a second window of thesecond display, and wherein the first window and the second window havedifferent display characteristics based on an output of the mathematicaloptimization engine. In another illustrative embodiment, the systemadjusts how the optimal decision set is displayed based on a limitationof a particular display used by the user.

FIG. 45 is a flowchart illustrating a process of generating andsequencing an optimal decision set, in accordance with an illustrativeembodiment. The process shown in FIG. 45 can be implemented in a systemfor chaotic event management, such as system 3300 shown in FIG. 33 orsystem 3400 shown in FIG. 34. The system shown in FIG. 45 can beimplemented in one or more data processing systems, such as dataprocessing systems 104, 106, 110, 112, and 114 in FIG. 1, or dataprocessing system 200 shown in FIG. 2.

The process begins as the system uses a mathematical optimizationalgorithm to select a first optimal decision set for a user, wherein themathematical optimization algorithm takes as input a decision template,chaotic event information, at least one constraint, and a user profile(step 4500). The system then uses a heuristic algorithm to eliminate afirst subset of decisions, wherein the first subset of decisions is inthe first optimal decision set, wherein a second optimal decision set isformed, and wherein the second optimal decision set comprises the firstoptimal decision set less the first subset of decisions (step 4502).

The system then uses the mathematical optimization algorithm to select asequence in which decisions in the second optimal decision set are to beconsidered, wherein the mathematical optimization algorithm takes asinput the second optimal decision set, the decision template, thechaotic event information, the at least one constraint, and the userprofile (step 4504). The system then stores the sequence (step 4506).

Responsive to at least one of a received decision and a change in atleast one of the decision template, the chaotic event information, theat least one constraint, and the user profile, the system reiterates themathematical optimization algorithm to select a third optimal decisionset for the user, wherein the mathematical optimization algorithmreceives as further input the at least one of the decision and thechange (step 4508). The system then uses a heuristic algorithm toeliminate a second subset of decisions, wherein the second subset ofdecisions is in the third optimal decision set, wherein a fourth optimaldecision set is formed, and wherein the fourth optimal decision setcomprises the third optimal decision set less the second subset ofdecisions (step 4510).

The system then uses a mathematical optimization algorithm to select asecond sequence in which decisions in the fourth optimal decision setare to be considered, wherein the mathematical optimization algorithmtakes as input the fourth optimal decision set, the decision template,the chaotic event information, the at least one constraint, and the userprofile (step 4512). The system then stores the second sequence (step4514), and terminates thereafter.

In an illustrative embodiment, when using the mathematical optimizationalgorithm to select the sequence, the mathematical optimization enginefurther takes as input the first subset of decisions. In this manner,the process can be massively recursively performed using as input outputof previous iterations of the system.

The illustrative embodiments described herein solve many of the problemsfaced by decision makers charged with the responsibility of respondingto a chaotic event. Particularly with respect to responding to largescale chaotic events, such as a major hurricane like Hurricane Katrina,decision makers are faced with an overwhelming number of decisions basedon limited information. Even when information is available, limited timeis available in which to make decisions. When time is limited,information that is particularly relevant may not present itself to thedecision makers in a short enough time. Additionally, lower leveldecision makers may not know to whom to turn for instructions. Theillustrative embodiments described herein provide a mechanism to solveall of these problems.

VI. Conclusion

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes firmware, resident software,microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer implemented method for displaying information related to achaotic event, the computer implemented method comprising: using amathematical optimization algorithm to select an optimal decision setfor a user, wherein the mathematical optimization algorithm takes asinput a decision template, chaotic event information regarding a chaoticevent, and a user profile; and displaying the optimal decision set forthe user.
 2. The computer implemented method of claim 1 furthercomprising: responsive to a change in at least one of the decisiontemplate, the chaotic event information, and the user profile,reiterating the mathematical optimization algorithm to select a secondoptimal decision set for the user, wherein the mathematical optimizationalgorithm receives as further input the change; and displaying thesecond optimal decision set for the user.
 3. The computer implementedmethod of claim 1 wherein displaying is performed in a display window ofa data processing system, and wherein the method further comprises:altering the display window based on an output of the mathematicaloptimization algorithm, whereby the display is altered according to atleast one of the decision template, the chaotic event information, theuser profile, and combinations thereof.
 4. The computer implementedmethod of claim 1 further comprising: receiving resource informationregarding resources useful for responding to the chaotic event, whereinthe mathematical optimization algorithm takes as further input theresource information.
 5. The computer implemented method of claim 1wherein the user profile comprises at least one element selected fromthe group consisting of: a skill of the user, user input, an educationof the user, a limitation of the user, a cultural fact regarding theuser, a political influence of the user, and a rank of a user, andwherein the at least one element influences how the mathematicaloptimization algorithm selects the optimal decision set.
 6. The computerimplemented method of claim 1 further comprising: receiving a decision;reiterating the mathematical optimization algorithm to select a secondoptimal decision set, wherein the mathematical optimization algorithmtakes as further input the decision; and displaying the second optimaldecision set for the user.
 7. The computer implemented method of claim 1further comprising: monitoring for a change in the chaotic eventinformation; and responsive to detecting the change, reiterating themathematical optimization algorithm to select a second optimal decisionset, wherein the mathematical optimization algorithm takes as furtherinput the change; and displaying the second optimal decision set for theuser.
 8. The computer implemented method of claim 1 further comprising:receiving information that a second user is a decision maker with regardto the chaotic event, wherein the second user has a second user profile;and reiterating the mathematical optimization algorithm to select asecond optimal decision set for the user and a third optimal decisionset for the second user, wherein the mathematical optimization algorithmtakes as further input the second user profile; displaying the secondoptimal decision set on a first display associated with the user, andwherein the second optimal decision set is further displayed on a firstwindow of the first display; and displaying the third optimal decisionset on a second display associated with the second user, wherein thethird optimal decision set is further displayed on a second window ofthe second display, and wherein the first window and the second windowhave different display characteristics based on an output of themathematical optimization engine.
 9. The computer implemented method ofclaim 1 further comprising: adjusting how the optimal decision set isdisplayed based on a limitation of a particular display used by theuser.
 10. A computer program product comprising: a computer usablemedium having computer usable program code for displaying informationrelated to a chaotic event, the computer program product including:computer usable program code for using a mathematical optimizationalgorithm to select an optimal decision set for a user, wherein themathematical optimization algorithm takes as input a decision template,chaotic event information regarding a chaotic event, and a user profile;and computer usable program code for displaying the optimal decision setfor the user.
 11. The computer program product of claim 10 furthercomprising: computer usable program code for, responsive to a change inat least one of the decision template, the chaotic event information,and the user profile, reiterating the mathematical optimizationalgorithm to select a second optimal decision set for the user, whereinthe mathematical optimization algorithm receives as further input thechange; and computer usable program code for displaying the secondoptimal decision set for the user.
 12. The computer program product ofclaim 10 wherein displaying is performed in a display window of a dataprocessing system, and wherein the method further comprises: computerusable program code for altering the display window based on an outputof the mathematical optimization algorithm, whereby the display isaltered according to at least one of the decision template, the chaoticevent information, the user profile, and combinations thereof.
 13. Thecomputer program product of claim 10 further comprising: computer usableprogram code for receiving resource information regarding resourcesuseful for responding to the chaotic event, wherein the mathematicaloptimization algorithm takes as further input the resource information.14. The computer program product of claim 10 wherein the user profilecomprises at least one element selected from the group consisting of: askill of the user, user input, an education of the user, a limitation ofthe user, a cultural fact regarding the user, and a political influenceof the user, and a rank of a user, and wherein the at least one elementinfluences how the mathematical optimization algorithm selects theoptimal decision set.
 15. The computer program product of claim 10further comprising: receiving a decision; reiterating the mathematicaloptimization algorithm to select a second optimal decision set, whereinthe mathematical optimization algorithm takes as further input thedecision; and displaying the second optimal decision set for the user.16. The computer program product of claim 10 further comprising:monitoring for a change in the chaotic event information; and responsiveto detecting the change, reiterating the mathematical optimizationalgorithm to select a second optimal decision set, wherein themathematical optimization algorithm takes as further input the change;and displaying the second optimal decision set for the user.
 17. Thecomputer program product of claim 10 further comprising: receivinginformation that a second user is a decision maker with regard to thechaotic event, wherein the second user has a second user profile; andreiterating the mathematical optimization algorithm to select a secondoptimal decision set for the user and a third optimal decision set forthe second user, wherein the mathematical optimization algorithm takesas further input the second user profile; displaying the second optimaldecision set on a first display associated with the user, and whereinthe second optimal decision set is further displayed on a first windowof the first display; and displaying the third optimal decision set on asecond display associated with the second user, wherein the thirdoptimal decision set is further displayed on a second window of thesecond display, and wherein the first window and the second window havedifferent display characteristics based on an output of the mathematicaloptimization engine.
 18. The computer program product of claim 10further comprising: adjusting how the optimal decision set is displayedbased on a limitation of a particular display used by the user.
 19. Adata processing system comprising: a bus; at least one processor coupledto the bus; a computer usable medium coupled to the bus, wherein thecomputer usable medium contains a set of instructions for displayinginformation related to a chaotic event, wherein the at least oneprocessor is adapted to carry out the set of instructions to: use amathematical optimization algorithm to select an optimal decision setfor a user, wherein the mathematical optimization algorithm takes asinput a decision template, chaotic event information regarding a chaoticevent, and a user profile; and display the optimal decision set for theuser.
 20. The data processing system of claim 19 wherein displaying isperformed in a display window of the data processing system, and whereinthe at least one processor is further adapted to carry out the set ofinstructions to: alter the display window based on an output of themathematical optimization algorithm, whereby the display is alteredaccording to at least one of the decision template, the chaotic eventinformation, the user profile, and combinations thereof.